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Pedestrian detection is notoriously considered a challenging task due to the frequent occlusion between humans. Unlike generic object detection, pedestrian detection involves a single category but dense instances, making it crucial to achieve accurate and robust object localization. By analogizing instance-level localization to a variational autoencoder and regarding the dense proposals as the latent variables, we establish a unique perspective of formulating pedestrian detection as a variational inference problem. From this vantage, we propose the Variational Pedestrian Detector (VPD), which uses a probabilistic model to estimate the true posterior of inferred proposals and applies a reparameterization trick to approximate the expected detection likelihood. In order to adapt the variational inference problem to the case of pedestrian detection, we propose a series of customized designs to cope with the issue of occlusion and spatial vibration. Specifically, we propose the Normal Gaussian and its variant of the Mixture model to parameterize the posterior in complicated scenarios. The inferred posterior is regularized by a conditional prior related to the ground-truth distribution, thus directly coupling the latent variables to specific target objects. Based on the posterior distribution, maximum detection likelihood estimation is applied to optimize the pedestrian detector, where a lightweight statistic decoder is designed to cast the detection likelihood into a parameterized form and enhance the confidence score estimation. With this variational inference process, VPD endows each proposal with the discriminative ability from its adjacent distractor due to the disentangling nature of the latent variable in variational inference, achieving accurate and robust detection in crowded scenes. Experiments conducted on CrowdHuman, CityPersons, and MS COCO demonstrate that our method is not only plug-and-play for numerous popular single-stage methods and two-stage methods but also can achieve a remarkable performance gain in highly occluded scenarios. The code for this project can be found at https://github.com/hhy-ee/VPD. [ABSTRACT FROM AUTHOR]

To address the issues of insufficient samples, limited scene diversity, missing perspectives, and low resolution in existing UAV-based pedestrian detection datasets, this paper proposes a novel UAV-based pedestrian detection benchmark dataset named the Novel Surveillance View (NSV). This dataset encompasses diverse scenes and pedestrian information captured from multiple perspectives, and introduces an innovative data mining approach that leverages tracking and optical flow information. This approach significantly improves data acquisition efficiency while ensuring annotation quality. Furthermore, an improved pedestrian detection method is proposed to overcome the performance degradation caused by significant perspective changes in top-down UAV views. Firstly, the View-Agnostic Decomposition (VAD) module decouples features into perspective-dependent and perspective-independent branches to enhance the model's generalization ability to perspective variations. Secondly, the Deformable Conv-BN-SiLU (DCBS) module dynamically adjusts the receptive field shape to better adapt to the geometric deformations of pedestrians. Finally, the Context-Aware Pyramid Spatial Attention (CPSA) module integrates multi-scale features with attention mechanisms to address the challenge of drastic target scale variations. The experimental results demonstrate that the proposed method improves the mean Average Precision (mAP) by 9% on the NSV dataset, thereby validating that the approach effectively enhances pedestrian detection accuracy from UAV perspectives by optimizing perspective features. [ABSTRACT FROM AUTHOR]

With the rapid development of tourism, the concentration of visitor flows poses significant challenges for public safety management, especially in low-light and highly occluded environments, where existing pedestrian detection technologies often struggle to achieve satisfactory accuracy. Although infrared images perform well under low-light conditions, they lack color and detail, making them susceptible to background noise interference, particularly in complex outdoor environments where the similarity between heat sources and pedestrian features further reduces detection accuracy. To address these issues, this paper proposes the FusionU10 model, which combines information from both infrared and visible light images. The model first incorporates an Attention Gate mechanism (AGUNet) into an improved UNet architecture to focus on key features and generate pseudo-color images, followed by pedestrian detection using YOLOv10. During the prediction phase, the model optimizes the loss function with Complete Intersection over Union (CIoU), objectness loss (obj loss), and classification loss (cls loss), thereby enhancing the performance of the detection network and improving the quality and feature extraction capabilities of the pseudo-color images through a feedback mechanism. Experimental results demonstrate that FusionU10 significantly improves detection accuracy and robustness in complex scenes on the FLIR, M3FD, and LLVIP datasets, showing great potential for application in challenging environments. [ABSTRACT FROM AUTHOR]

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Object detection in images is a fundamental component of many safety-critical systems, such as autonomous driving, video surveillance systems, and robotics. Adversarial patch attacks, being easily implemented in the real world, provide effective counteraction to object detection by state-of-the-art neural-based detectors. It poses a serious danger in various fields of activity. Existing defense methods against patch attacks are insufficiently effective, which underlines the need to develop new reliable solutions. In this manuscript, we propose a method which helps to increase the robustness of neural network systems to the input adversarial images. The proposed method consists of a Deep Convolutional Neural Network to reconstruct a benign image from the adversarial one; a Calculating Maximum Error block to highlight the mismatches between input and reconstructed images; a Localizing Anomalous Fragments block to extract the anomalous regions using the Isolation Forest algorithm from histograms of images' fragments; and a Clustering and Processing block to group and evaluate the extracted anomalous regions. The proposed method, based on anomaly localization, demonstrates high resistance to adversarial patch attacks while maintaining the high quality of object detection. The experimental results show that the proposed method is effective in defending against adversarial patch attacks. Using the YOLOv3 algorithm with the proposed defensive method for pedestrian detection in the INRIAPerson dataset under the adversarial attacks, the mAP50 metric reaches 80.97% compared to 46.79% without a defensive method. The results of the research demonstrate that the proposed method is promising for improvement of object detection systems security. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid promotion of autonomous driving technology, it is extremely important to scientifically anticipate the related technologies and analyze their possible impact on urban road systems. The accuracy of detection and localization of traffic elements of autonomous driving is closely related to the ability of autonomous driving devices to make control decisions and the safety of autonomous driving. The study designs a new high-speed road driving scheme based on autonomous driving by analyzing the challenges related to urban traffic that may be brought about by unmanned driving. On the basis of the faster R-CNN algorithm, the context information around the target is introduced to locate and detect small-scale traffic signs. A new pedestrian detection model is designed, which is based on the feature pyramid network and introduces the SE module to highlight the features of the visible part of the pedestrian and reduce the missed detection rate caused by inter-class occlusion. The improved traffic sign detection framework improves the detection accuracy by 18.91% compared to the original faster R-CNN, while the enhanced pedestrian inspection method improves the detection accuracy by 14.00%. For both traffic sign detection and pedestrian detection accuracy and speed are improved compared to the original method. [ABSTRACT FROM AUTHOR]

In traffic scenes, pedestrian target detection faces significant issues of misdetection and omission due to factors such as crowd density and obstacle occlusion. To address these challenges and enhance detection accuracy, we propose an improved CCW-YOLO algorithm. The algorithm first introduces a lightweight convolutional layer using GhostConv and incorporates an enhanced C2f module to improve the network's detection performance. Additionally, it integrates the Coordinate Attention module to better capture key points of the targets. Next, the bounding box loss function CIoU loss at the output of YOLOv5 is replaced with WiseIoU loss to enhance adaptability to various detection scenarios, thereby further improving accuracy. Finally, we develop a pedestrian count detection system using PyQt5 to enhance human–computer interaction. Experimental results on the INRIA public dataset showed that our algorithm achieved a detection accuracy of 98.4%, representing a 10.1% improvement over the original YOLOv5s algorithm. This advancement significantly enhances the detection of small objects in images and effectively addresses misdetection and omission issues in complex environments. These findings have important practical implications for ensuring traffic safety and optimizing traffic flow. [ABSTRACT FROM AUTHOR]

With the development of autonomous vehicles and intelligent transportation, more accurate detection of pedestrians. However, pedestrian detection suffers from occlusion and small target. First, the HorNet to improve the higher-order spatial interaction capability of the model, expand the effective sensory field, and enhance the feature extraction of pedestrians. Then, ODConv to gain the variability of each dimension and capture rich information. Finally, a layer to increase the accuracy of detecting pedestrians at small scales. We optimize the regression prediction of the anchor using the Efficient IOU Loss (EIOU) function. Experimental data show that the mean average precision (mAP) of the HOD-YOLOv5 model achieves 83.5%, compared to YOLOv5, which is 4.4% higher than original YOLOv5, and the recognition speed of the HOD-YOLOV5 reaches 106.7 frames per second (FPS). This demonstrates that the proposed model could realize real-time pedestrian detection at a relatively small cost, which satisfies the requirements of uncrewed and intelligent transportation. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Achieving a balance between high detection accuracy and lightweight network models poses a significant challenge in pedestrian detection. To address these issues, we introduce GSDC Net, an enhanced architecture inspired by the robust YOLOv7 model. Firstly, G-SDC module is designed by integrating smoothed dilated convolutions to bolster detection accuracy while simultaneously trimming the network's computational footprint. And then, the SDCPPCSPC Module is advanced by employing Smoothed dilated convolutions to expand the receptive field, thereby capturing richer contextual information. To bolster feature fusion and augment the model's capabilities, the bidirectional feature pyramid network and shuffle attention mechanisms are introduced. Through comparative experiments on the crowd human dataset, we have substantiated the effectiveness of our proposed model. The results indicate a notable improvement in mean average precision (mAP), reaching 83.88%. Additionally, the model's size, and frames per second (FPS) have been optimized to 52.35 MB, and 82, respectively. These enhancements confirm the model's exceptional balance among detection accuracy, inference speed, and model compactness. [ABSTRACT FROM AUTHOR]

This paper surveys real-time object detection literature critically and analytically, focusing particularly on pedestrian detection for safe autonomous vehicles. It addresses the challenges in the domain, some of the sources of which are variations in age, gender, clothing, lighting, backgrounds, and occlusion. The paper reviews object detection algorithms after providing an overview of deep learning basics and main architectures of neural networks, followed by discussion on existing algorithms along with their strengths, weaknesses, and future research directions. There is a need for pedestrian detection datasets with further complex annotations and multi-source integration, which captures interactions between pedestrians and their surroundings. Incorporating advanced sensors, including LiDAR, infrared, and depth sensors, as the foremost means to enhance the detection capabilities in more adverse conditions, such as low-light situations and occlusion. However, architectures such as YOLO, SSD, and Faster R-CNN, which have led to current improvements in performance, still allow room for improving pedestrian detection accuracy. By filling in these insights and proposed solutions, the paper focus on the development of pedestrian detection technology, how it can be brought into a safer, reliable, real-world applicability towards the system of autonomous driving. All of these results point to continued innovation towards deep learning, multi-sensor integration, and developing datasets to achieve optimal performance levels in real world conditions for autonomous driving systems.

The increasing availability of ubiquitous sensor data on the built environment holds great potential for a new generation of travel and mobility research. Bluetooth technology, for example, is already vastly used in vehicular transportation management solutions and services. Current studies discuss the potential of this emerging technology for pedestrian mobility research, but it has yet to be examined in a large urban setting. One of the main problems is detecting pedestrians from Bluetooth records since their behavior and movement patterns share similarities with other urban transportation modes. This study aims to accurately detect pedestrians using a network of 65 Bluetooth detectors located in Tel-Aviv, Israel, which record on average over 60,000 unique daily Bluetooth Media-Access-Control addresses. We propose a detection methodology that includes system calibration, effective travel time calculation, and classification by velocity that takes into consideration the probability of vehicular traffic jams. An evaluation of the proposed methodology presents a promising pedestrian detection accuracy rate of 89%. We showcase the results of pedestrian traffic analysis, together with a discussion on the data analysis challenges and limitations. To the best of our knowledge, this work is the first to analyze pedestrian records detection from a Bluetooth network employed in a dynamic urban environment setting.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: Pedestrian detection is very important for the environment perception and safety action of intelligent robots and autonomous driving, and is the key to ensuring the safe action of intelligent robots and auto assisted driving. Methods: In response to the characteristics of pedestrian objects occupying a small image area, diverse poses, complex scenes and severe occlusion, this paper proposes an improved pedestrian object detection method based on the YOLOv7 model, which adopts the Convolutional Block Attention Module (CBAM) attention mechanism and Deformable ConvNets v2 (DCNv2) in the two Efficient Layer Aggregation Network (ELAN) modules of the backbone feature extraction network. In addition, the detection head is replaced with a Dynamic Head (DyHead) detector head with an attention mechanism; unnecessary background information around the pedestrian object is also effectively excluded, making the model learn more concentrated feature representations. Results: Compared with the original model, the log-average miss rate of the improved YOLOv7 model is significantly reduced in both the Citypersons dataset and the INRIA dataset. Conclusions: The improved YOLOv7 model proposed in this paper achieved good performance improvement in different pedestrian detection problems. The research in this paper has important reference significance for pedestrian detection in complex scenes such as small, occluded and overlapping objects. [ABSTRACT FROM AUTHOR]

Objective: Automatic emergency braking systems with pedestrian detection (PAEB) are effective at preventing pedestrian crashes, but the safety benefits are not observed at night. This study used the Insurance Institute for Highway Safety (IIHS) PAEB test data to characterize PAEB responses in different lighting conditions and for different rated systems. Methods: Data from 6,919 IIHS PAEB tests were retrieved from IIHS databases. Invalid trials, trials without AEB, and trials with outliers were removed leaving 5,894 trials from 212 model year 2018 to 2023 vehicles for analysis. PAEB responses were characterized by computing the time-to-collision (TTC) of forward collision warning (FCW) and AEB; brake threat number (BTN); mean deceleration; maximum deceleration; and maximum jerk. A linear mixed-effects model was used to predict each dependent measure with scenario (crossing adult, stationary adult), speed, rating (superior, basic/advanced), lighting (day, night with high beams, night with low beams), and their interactions. Vehicle was included as a random effect. A Bonferroni correction was applied to maintain a family-wise type-1 error rate of 0.05 across 138 total hypothesis tests. Results: PAEB system warnings were later and automatic braking occurred later as speed increased at night with low beams but changed little at night with high beams and during the day (p < 0.0004). BTN increased more rapidly as speed increased at night with low beams compared with high beams and during the day (p < 0.0004). Based on the BTN model, on average, PAEB systems can brake to avoid the adult mannequin (BTN < 1) when closing speed is less than 67 km/h during the day and at night with high beams but only when closing speed is less than 49 km/h at night with low beams. Superior-rated PAEB systems warned and braked earlier compared with basic/advanced-rated systems (p < 0.0004). Conclusions: Increased lighting from high beams made nighttime performance resemble daytime performance in controlled testing. Increasing output from vehicle low beams, increasing the use of high beams, and enhancing overhead lighting around crosswalks and pedestrian areas are all methods for increasing lighting, improving pedestrian conspicuity, and enhancing PAEB performance to prevent pedestrian crashes. [ABSTRACT FROM AUTHOR]

Pedestrian detection is a critical task in computer vision; however, mainstream algorithms often struggle to achieve high detection accuracy in complex scenarios, particularly due to target occlusion and the presence of small objects. This paper introduces a novel pedestrian detection algorithm, YOLO-ESL, based on the YOLOv7 framework. YOLO-ESL integrates the ELAN-SA module, designed to enhance feature extraction, with the LGA module, which improves feature fusion. The ELAN-SA module optimizes the flexibility and efficiency of small object feature extraction, while the LGA module effectively integrates multi-scale features through local and global attention mechanisms. Additionally, the CIOUNMS algorithm addresses the issue of target loss in cases of high overlap, improving boundary box filtering. Evaluated on the VOC2012 pedestrian dataset, YOLO-ESL achieved an accuracy of 93.7%, surpassing the baseline model by 3.0%. Compared to existing methods, this model not only demonstrates strong performance in handling occluded and small object detection but also remarkable robustness and efficiency. [ABSTRACT FROM AUTHOR]

Occlusion presents a major obstacle in the development of pedestrian detection technologies utilizing computer vision. This challenge includes both inter-class occlusion caused by environmental objects obscuring pedestrians, and intra-class occlusion resulting from interactions between pedestrians. In complex and variable urban settings, these compounded occlusion patterns critically limit the efficacy of both one-stage and two-stage pedestrian detectors, leading to suboptimal detection performance. To address this, we introduce a novel architecture termed the Attention-Guided Feature Enhancement Network (AGFEN), designed within the deep convolutional neural network framework. AGFEN improves the semantic information of high-level features by mapping it onto low-level feature details through sampling, creating an effect comparable to mask modulation. This technique enhances both channel-level and spatial-level features concurrently without incurring additional annotation costs. Furthermore, we transition from a traditional one-to-one correspondence between proposals and predictions to a one-to-multiple paradigm, facilitating non-maximum suppression using the prediction set as the fundamental unit. Additionally, we integrate these methodologies by aggregating local features between regions of interest (RoI) through the reuse of classification weights, effectively mitigating false positives. Our experimental evaluations on three widely used datasets demonstrate that AGFEN achieves a 2.38% improvement over the baseline detector on the CrowdHuman dataset, underscoring its effectiveness and potential for advancing pedestrian detection technologies. [ABSTRACT FROM AUTHOR]

The advancements in deep learning have significantly enhanced the accuracy and robustness of pedestrian detection. However, recent studies reveal that adversarial attacks can exploit the vulnerabilities of deep learning models to mislead detection systems. These attacks are effective not only in digital environments but also pose significant threats to the reliability of pedestrian detection systems in the physical world. Existing adversarial attacks targeting pedestrian detection primarily focus on daytime scenarios and are easily noticeable by road observers. In this paper, we propose a novel adversarial attack method against vehicle–pedestrian detection systems at night. Our approach utilizes reflective optical materials that can effectively reflect light back to its source. We optimize the placement of these reflective patches using the particle swarm optimization (PSO) algorithm and deploy patches that blend with the color of pedestrian clothing in real-world scenarios. These patches remain inconspicuous during the day or under low-light conditions, but at night, the reflected light from vehicle headlights effectively disrupts the vehicle's pedestrian detection systems. Considering that real-world detection models are often black-box systems, we propose a "symmetry" strategy, which involves using the behavior of an alternative model to simulate the response of the target model to adversarial patches. We generate adversarial examples using YOLOv5 and apply our attack to various types of pedestrian detection models. Experiments demonstrate that our approach is both effective and broadly applicable. [ABSTRACT FROM AUTHOR]

The incidence of right-turning pedestrian accidents is increasing in South Korea. Most of the accidents occur when a large vehicle is turning right, and the main cause of the accidents was found to be the driver's limited field of vision. After these accidents, the government implemented a series of institutional measures with the objective of preventing such accidents. However, despite the institutional arrangements in place, pedestrian accidents continue to occur. We focused on the many limitations that autonomous vehicles, like humans, can face in such situations. To address this issue, we propose a right-turn pedestrian collision avoidance system by installing a LiDAR sensor in the center of the intersection to facilitate pedestrian detection. Furthermore, the urban road environment is considered, as this provides the optimal conditions for the model to perform at its best. During this research, we collected data on right-turn accidents using the CARLA simulator and ROS interface and demonstrated the effectiveness of our approach in preventing such incidents. Our results suggest that the implementation of this method can effectively reduce the incidence of right-turn accidents in autonomous vehicles. [ABSTRACT FROM AUTHOR]

The increasing availability of ubiquitous sensor data on the built environment holds great potential for a new generation of travel and mobility research. Bluetooth technology, for example, is already vastly used in vehicular transportation management solutions and services. Current studies discuss the potential of this emerging technology for pedestrian mobility research, but it has yet to be examined in a large urban setting. One of the main problems is detecting pedestrians from Bluetooth records since their behavior and movement patterns share similarities with other urban transportation modes. This study aims to accurately detect pedestrians using a network of 65 Bluetooth detectors located in Tel-Aviv, Israel, which record on average over 60,000 unique daily Bluetooth Media-Access-Control addresses. We propose a detection methodology that includes system calibration, effective travel time calculation, and classification by velocity that takes into consideration the probability of vehicular traffic jams. An evaluation of the proposed methodology presents a promising pedestrian detection accuracy rate of 89%. We showcase the results of pedestrian traffic analysis, together with a discussion on the data analysis challenges and limitations. To the best of our knowledge, this work is the first to analyze pedestrian records detection from a Bluetooth network employed in a dynamic urban environment setting. [ABSTRACT FROM AUTHOR]

Connected cooperative and automated (CAM) vehicles and self-driving cars need to achieve robust and accurate environment understanding. With this aim, they are usually equipped with sensors and adopt multiple sensing strategies, also fused among them to exploit their complementary properties. In recent years, artificial intelligence such as machine learning- and deep learning-based approaches have been applied for object and pedestrian detection and prediction reliability quantification. This paper proposes a procedure based on the YOLOv8 (You Only Look Once) method to discover objects on the roads such as cars, traffic lights, pedestrians and street signs in foggy weather conditions. In particular, YOLOv8 is a recent release of YOLO, a popular neural network model used for object detection and image classification. The obtained model is applied to a dataset including about 4000 foggy road images and the object detection accuracy is improved by changing hyperparameters such as epochs, batch size and augmentation methods. To achieve good accuracy and few errors in detecting objects in the images, the hyperparameters are optimized by four different methods, and different metrics are considered, namely accuracy factor, precision, recall, precision–recall and loss. [ABSTRACT FROM AUTHOR]

Owing to the low detection accuracy of camera-based object detection models, various fusion techniques with Light Detection and Ranging (LiDAR) have been attempted. This has resulted in improved detection of objects that are difficult to detect due to partial occlusion by obstacles or unclear silhouettes. However, the detection performance remains limited in low-light environments where small pedestrians are located far from the sensor or pedestrians have difficult-to-estimate shapes. This study proposes an object detection model that employs a Gaussian–Sobel filter. This filter combines Gaussian blurring, which suppresses the effects of noise, and a Sobel mask, which accentuates object features, to effectively utilize depth maps generated by LiDAR for object detection. The model performs independent pedestrian detection using the real-time object detection model You Only Look Once v4, based on RGB images obtained using a camera and depth maps preprocessed by the Gaussian–Sobel filter, and estimates the optimal pedestrian location using non-maximum suppression. This enables accurate pedestrian detection while maintaining a high detection accuracy even in low-light or external-noise environments, where object features and contours are not well defined. The test evaluation results demonstrated that the proposed method achieved at least 1–7% higher average precision than the state-of-the-art models under various environments. [ABSTRACT FROM AUTHOR]

Pedestrian detection has recently attracted widespread attention as a challenging problem in computer vision. The accuracy of pedestrian detection is affected by differences in gestures, background clutter, local occlusion, differences in scales, pixel blur, and other factors occurring in real scenes. These problems lead to false and missed detections. In view of these visual description deficiencies, we leveraged pedestrian pose information as a supplementary resource to address the occlusion challenges that arise in pedestrian detection. An attention mechanism was integrated into the visual information as a supplement to the pose information, because the acquisition of pose information was limited by the pose estimation algorithm. We developed a pedestrian detection method that integrated an attention mechanism with visual and pose information, including pedestrian region generation and pedestrian recognition networks, effectively addressing occlusion and false detection issues. The pedestrian region proposal network was used to generate a series of candidate regions with possible pedestrian targets from the original image. Then, the pedestrian recognition network was used to judge whether each candidate region contained pedestrian targets. The pedestrian recognition network was composed of four parts: visual features, pedestrian poses, pedestrian attention, and classification modules. The visual feature module was responsible for extracting the visual feature descriptions of candidate regions. The pedestrian pose module was used to extract pose feature descriptions. The pedestrian attention module was used to extract attention information, and the classification module was responsible for fusing visual features and pedestrian pose descriptions with the attention mechanism. The experimental results on the Caltech and CityPersons datasets demonstrated that the proposed method could substantially more accurately identify pedestrians than current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Aiming at the YOLOv4 model's difficulty in dealing with occluded pedestrians in real scenarios, this paper made improvements in ensuring the real-time performance of the YOLOv4 model and applied the YOLOv4 model to pedestrian detection. In order to improve the model's ability to detect occluded pedestrians, the model adopted the K-means ++ clustering algorithm to re-design the priori frames applicable to the pedestrian target sizes, and introduced the exclusion loss function term to maximise the distance between the candidate frames and the neighbouring real frames of non-mate-hing targets, and minimise the overlap ratio between the candidate frames and the real frames of other targets. The improved model was experimented on the challenging datasets CrowdHuman and Caltech, and the experimental results verified the effectiveness of it. Finally the model has been applied to video pedestrian detection in real scenarios, which also verified the effectiveness of the improvements in this paper. [ABSTRACT FROM AUTHOR]

In order to achieve the protection of personal privacy, the author proposes research on sports data privacy protection and information security management. The author used the two-dimensional fractional Fourier transform (2D-FRFT) method to encrypt the detected human body parts, which can be decrypted when needed for viewing. Compared to traditional Fourier transform, Fractional Fourier Transform (FRFT) can better express the time-frequency characteristics of signals and is very sensitive to the order of the transform. It is widely used in image encryption systems. 2D-FRFT increases the range of keys, further enhancing the security of the system. The author achieved encryption by extracting the detected human body parts and then performing a certain order of FRFT in the x and y directions respectively; When decrypting, use the same order of encryption to perform inverse fractional Fourier transform. Finally, based on research on pedestrian detection and encryption technology, the author designed a human-machine interaction interface that integrates the functions of detection and encryption interfaces, making the entire operation more intuitive and concise. [ABSTRACT FROM AUTHOR]

Although pedestrian detection techniques are improving, this task is still challenging due to the problems of target occlusion, small targets, and complex pedestrian backgrounds in images of different scenes. As a result, the You Only Look Once algorithm exhibits lower detection accuracy. In this paper, a multisensor (MS) module that utilizes multiple dilated convolutions is proposed to sample feature images, avoiding information loss caused by repeated sampling, to improve the feature extraction and target detection performance of the algorithm. In addition, a lightweight shuffle-based efficient channel attention mechanism is introduced to conduct grouping in the channel dimension and perform parallel processing for each subfeature map channel. A new branch is introduced to enrich the channel feature information for multiscale feature representation. Finally, a distance intersection over union-based nonmaximum suppression (DIoU-NMS) method is introduced to minimize the occurrence of missed targets due to occlusion by taking the prediction box and ground truth box centroid locations information into account without increasing the computational cost over that of normal NMS. Our method is extensively evaluated on several challenging pedestrian detection datasets, achieving 87.73%, 34.7%, 93.96% and 95.23% mean average precision values on PASCAL VOC, MS COCO, Caltech Pedestrian and INRIA Person, which are respectively. The experimental results demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

With the rapid development of tourism, the concentration of visitor flows poses significant challenges for public safety management, especially in low-light and highly occluded environments, where existing pedestrian detection technologies often struggle to achieve satisfactory accuracy. Although infrared images perform well under low-light conditions, they lack color and detail, making them susceptible to background noise interference, particularly in complex outdoor environments where the similarity between heat sources and pedestrian features further reduces detection accuracy. To address these issues, this paper proposes the FusionU10 model, which combines information from both infrared and visible light images. The model first incorporates an Attention Gate mechanism (AGUNet) into an improved UNet architecture to focus on key features and generate pseudo-color images, followed by pedestrian detection using YOLOv10. During the prediction phase, the model optimizes the loss function with Complete Intersection over Union (CIoU), objectness loss (obj loss), and classification loss (cls loss), thereby enhancing the performance of the detection network and improving the quality and feature extraction capabilities of the pseudo-color images through a feedback mechanism. Experimental results demonstrate that FusionU10 significantly improves detection accuracy and robustness in complex scenes on the FLIR, M3FD, and LLVIP datasets, showing great potential for application in challenging environments.

With the development of autonomous vehicles and intelligent transportation, more accurate detection of pedestrians. However, pedestrian detection suffers from occlusion and small target. First, the HorNet to improve the higher-order spatial interaction capability of the model, expand the effective sensory field, and enhance the feature extraction of pedestrians. Then, ODConv to gain the variability of each dimension and capture rich information. Finally, a layer to increase the accuracy of detecting pedestrians at small scales. We optimize the regression prediction of the anchor using the Efficient IOU Loss (EIOU) function. Experimental data show that the mean average precision (mAP) of the HOD-YOLOv5 model achieves 83.5%, compared to YOLOv5, which is 4.4% higher than original YOLOv5, and the recognition speed of the HOD-YOLOV5 reaches 106.7 frames per second (FPS). This demonstrates that the proposed model could realize real-time pedestrian detection at a relatively small cost, which satisfies the requirements of uncrewed and intelligent transportation.

From Bluetooth 5.1, a direction finding function has been added that allows the receiver to measure the angle of arrival (AoA) of the wireless signal from the sender. In this letter, we propose a method for detecting pedestrians and their movement direction passing between Bluetooth devices equipped with direction finding functions. As a result of the evaluation, pedestrians could be detected with higher accuracy (F‐score 0.929) by combining received signal strength indicator and AoA than when using each alone. In addition, the accuracy rate for estimating the movement direction using AoA was 0.704. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In response to the problems of complex model networks, low detection accuracy, and the detection of small targets prone to false detections and omissions in pedestrian detection, this paper proposes FedsNet, a pedestrian detection network based on RT-DETR. By constructing a new lightweight backbone network, ResFastNet, the number of parameters and computation of the model are reduced to accelerate the detection speed of pedestrian detection. Integrating the Efficient Multi-scale Attention(EMA) mechanism with the backbone network creates a new ResBlock module for improved detection of small targets. The more effective DySample has been adopted as the upsampling operator to improve the accuracy and robustness of pedestrian detection. SIoU is used as the loss function to improve the accuracy of pedestrian recognition and speed up model convergence. Experimental evaluations conducted on a self-built pedestrian detection dataset demonstrate that the average accuracy value of the FedsNet model is 91 % , which is a 1.7 % improvement over the RT-DETR model. The parameters and model volume are reduced by 15.1 % and 14.5 % , respectively. When tested on the public dataset WiderPerson, FedsNet achieved the average accuracy value of 71.3 % , an improvement of 1.1 % over the original model. In addition, the detection speed of the FedsNet network reaches 109.5 FPS and 100.3 FPS, respectively, meeting the real-time requirements of pedestrian detection. [ABSTRACT FROM AUTHOR]

To eliminate the defects of incomplete detection and high false detection rate caused by insignificant pedestrian target features, dense small targets and complex background in infrared images, this paper proposes an infrared pedestrian target detection algorithm based on improved YOLOv7. Firstly, the original Spatial Pyramid Pooling (SPP) module is replaced by the Channel Attention based Spatial Pyramid Pooling (CASPP) module based on the YOLOv7-tiny model, so that the model could pay more attention to the extraction of pedestrian features; then, the convolution module CBM based on the Meta-ACON activation function is introduced, which further suppressed the background noise and preserved the details of the pedestrians; finally, an alpha fusion data enhancement method is proposed to enrich the diversity of samples and improve the stability of the model in complex environments. The validation based on the FLIR dataset shows that the proposed method improves the accuracy by 3% and reduces the computation by 38% compared with the YOLOv7-tiny algorithm, which is more suitable for infrared pedestrian target detection scenarios. [ABSTRACT FROM AUTHOR]

In response to the complex and diverse nature of the road traffic environment, where vehicle and pedestrian detection is prone to false and missed detections, this paper proposes a vehicle and pedestrian target detection algorithm YOLOv8-RC based on multi-scale feature fusion. Initially, the RCS-OSA module is introduced within the structure of the base network YOLOv8 to replace the original module, thereby enhancing and integrating the extracted feature information. Additionally, a lightweight Context-Aware Adaptive Feature Reorganization (CARAFE) is employed to replace the original upsampling operator, enhancing the network s capability for global multi-scale information fusion. Subsequently, a detection dataset consisting of 6 000 images of vehicle and pedestrian targets is constructed through public datasets and network collection. The algorithm s detection performance is quantitatively evaluated using accuracy, recall rate, mean Average Precision at a 50% intersection over union threshold (mAP50), and mAP50-95. Compared to YOLOv8-N, YOLOv8-RC demonstrates an improvement of 1.7 percentage in accuracy, 1.2 percentage in recall rate, 0.9 percentage in mAP50, and 0.5 percentage in mAP50-95, thus validating the algorithm's effectiveness. [ABSTRACT FROM AUTHOR]

In crowded pedestrian detection, occlusion situations are common challenges that seriously impact detection performance. These occlusions are usually classified into pedestrian-to-pedestrian occlusions and object-to-pedestrian occlusions which result in false detection and missed detection. In this paper, we propose a novel model to address the crowded pedestrian detections in the cases of occlusions, which can generate an optimal bounding box containing the pedestrian instance with accurate position information. Firstly, Distance-Intersection over Union loss is introduced in Region Proposal Networks module for network training to generate proposal boxes, considering both the position and area of the region where the pedestrian is occluded. Secondly, a refinement module is added in Region Convolutional Neural Network to eliminate false positive proposal boxes, Earth Mover's Distance Loss is used to re-predict the pedestrian in these boxes. Finally, Relocation Non-Maximum Suppression is employed to select the optimal bounding box. Considering the parts of the pedestrian contained by its adjacent proposal boxes, the optimal bounding box is located in order to achieve the complete pedestrian instance. The proposed model is evaluated on CrowdHuman and CityPersons datasets respectively. On CrowdHuman dataset, the proposed model improves AP by 5.6% and JI by 5.2%, while reducing MR−2 by 3.8% compared to the baseline. Compared to the state-of-the-art model, the proposed model reduces 0.4% on MR−2, which shows its effectiveness for pedestrian detection in crowded scenes. On CityPersons dataset, the proposed model obtains the AP with 96.8% among all the evaluated models, which indicates its generalization for pedestrian detections in various crowded scenes. [ABSTRACT FROM AUTHOR]

In large public places such as railway stations and airports, dense pedestrian detection is important for safety and security. Deep learning methods provide relatively effective solutions but still face problems such as feature extraction difficulties, image multi-scale variations, and high leakage detection rates, which bring great challenges to the research in this field. In this paper, we propose an improved dense pedestrian detection algorithm GR-yolo based on Yolov8. GR-yolo introduces the repc3 module to optimize the backbone network, which enhances the ability of feature extraction, adopts the aggregation–distribution mechanism to reconstruct the yolov8 neck structure, fuses multi-level information, achieves a more efficient exchange of information, and enhances the detection ability of the model. Meanwhile, the Giou loss calculation is used to help GR-yolo converge better, improve the detection accuracy of the target position, and reduce missed detection. Experiments show that GR-yolo has improved detection performance over yolov8, with a 3.1% improvement in detection means accuracy on the wider people dataset, 7.2% on the crowd human dataset, and 11.7% on the people detection images dataset. Therefore, the proposed GR-yolo algorithm is suitable for dense, multi-scale, and scene-variable pedestrian detection, and the improvement also provides a new idea to solve dense pedestrian detection in real scenes. [ABSTRACT FROM AUTHOR]

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This research presents an innovative approach to pedestrian detection in video surveillance, leveraging the power of YOLOv5 (You Only Look Once version 5) combined with light perception fusion-based feature extraction. The proposed methodology aims to enhance the accuracy and efficiency of pedestrian detection systems in varying lighting conditions. YOLOv5, known for its real-time object detection capabilities, is integrated with a novel feature extraction technique that fuses information from multiple light perception sensors. This fusion strategy allows the model to adapt and perform robustly in diverse lighting scenarios. The experimental results demonstrate the superiority of the proposed method, achieving a remarkable performance. The fusion of YOLOv5 with light perception-based feature extraction showcases promising advancements in pedestrian detection, addressing challenges posed by dynamic lighting conditions in real-world surveillance environments. [ABSTRACT FROM AUTHOR]