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To address high miss rates and low detection accuracy caused by object scale variations and occlusion in video pedestrian detection, this study proposes a General Tracking-Information-Aided Detection Framework (GTIADF). The framework integrates object detection and tracking, leveraging inter-frame temporal information to improve robustness against scale changes and occlusion. GTIADF consists of two main components: (1) an enhanced multi-object tracking algorithm, Deep SORT+, which incorporates advanced modules, camera motion compensation, and DEMA for improved tracking robustness; and (2) an aided detection module that reduces missed detections via validation, interpolation, and re-scoring. The framework is adaptable and can be seamlessly integrated with other detectors to enhance their performance. Based on GTIADF, we propose the D3F-YOLO algorithm, which enhances the feature extraction module by using deformable convolution for better detection at varying scales. Additionally, a focusing diffusion pyramid network is introduced to improve multiscale feature representation, and the loss function is optimized to boost accuracy. Experiments on the Caltech dataset show that the proposed method achieves a mean mean precision (mAP) of 65. 7% and a miss rate of 32.4%, confirming its effectiveness in challenging scenarios. [ABSTRACT FROM AUTHOR]

With the rapid advancement of hyperspectral imaging technology, hyperspectral object tracking (HOT) has become a research hotspot in the field of remote sensing. Advanced HOT methods have been continuously proposed and validated on scarce datasets in recent years, which can be roughly divided into handcrafted feature-based methods and deep feature-based methods. Compared with methods via handcrafted features, deep feature-based methods can extract highly discriminative semantic features from hyperspectral images (HSIs) and achieve excellent tracking performance, making them more favored by the hyperspectral tracking community. However, deep feature-based HOT still faces challenges such as data-hungry, band gap, low tracking efficiency, etc. Therefore, it is necessary to conduct a thorough review of current trackers and unresolved problems in the HOT field. In this survey, we systematically classify and conduct a comprehensive analysis of 13 state-of-the-art deep feature-based hyperspectral trackers. First, we classify and analyze the trackers based on the framework and tracking process. Second, the trackers are compared and analyzed in terms of tracking accuracy and speed on two datasets for cross-validation. Finally, we design a specialized experiment for small object tracking (SOT) to further validate the tracking performance. Through in-depth investigation, the advantages and weaknesses of current HOT technology based on deep features are clearly demonstrated, which also points out the directions for future development. [ABSTRACT FROM AUTHOR]

This paper presents the results of the Move Over law compliance study. This study was carried out for The Federal Highway Administration in cooperation with ten State Highway agencies that provided the data (video recordings). This paper describes an outline of the system that was invented, developed, and applied to determine Move Over law compliance, as well as the initial analysis of the impact of various factors on compliance. In order to carry out the analysis, we processed 68 videos that contained over 33,000 vehicles. The median compliance with the Move Over law was 42.5% and varied heavily depending on diverse factors. This study makes two key contributions: first, it introduces an automated deep learning-based system that detects and evaluates Move Over law compliance by leveraging object detection and tracking technologies. Second, it presents a large-scale, multi-state compliance assessment, providing new empirical insights into driver behavior across various incident conditions. These findings offer a data-driven foundation for refining Move Over laws, enhancing public awareness efforts, and improving enforcement strategies. [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.)

To address the challenges of low accuracy and the difficulty in balancing a large field of view and long distance when tracking high-speed moving targets with a single sensor, an ROI adaptive digital zoom tracking method is proposed. In this paper, we discuss the impact of ROI on image processing and describe the design of the ROI adaptive digital zoom tracking system. Additionally, we construct an adaptive ROI update model based on normalized target information. To capture target changes effectively, we introduce the multi-scale regional measure and propose an improved particle filter algorithm, referred to as the improved multi-scale regional measure resampling particle filter (IMR-PF). This method enables high temporal resolution processing efficiency within a high-resolution large field of view, which is particularly beneficial for high-resolution videos. The IMR-PF can maintain high temporal resolution within a wide field of view with high resolution. Simulation results demonstrate that the improved target tracking method effectively improves tracking robustness to target motion changes and reduces the tracking center error by 20%, as compared to other state-of-the-art methods. The IMR-PF still maintains good performance even when confronted with various interference factors and in real-world scenario applications. [ABSTRACT FROM AUTHOR]

Airborne visual tracking is pivotal in enhancing the autonomy and intelligence of micro aerial vehicles (MAVs). However, MAVs frequently encounter challenges such as viewpoint changes and interference from similar objects in practice. Additionally, due to their small size and lightweight characteristics, MAVs have limited onboard computational resources, significantly constraining algorithm complexity and impacting tracking performance. To address these issues, we propose a robust and lightweight tracking model, self-adaptive dynamic template Siamese network (SiamSDT). Leveraging two key designs: temporal attention mechanism and Self-adaptive Template Fusion module, SiamSDT is capable of adapting to the appearance variations during the tracking process. Specifically, temporal attention mechanism integrates historical information in a sequential manner, retaining pertinent information while reducing storage and computational complexity. Additionally, the Self-adaptive Template Fusion module dynamically adjusts the fusion ratio of each template through a similarity matrix, further enhancing the model’s adaptability and anti-interference capability. Furthermore, we propose a solution tailored for heterogeneous ZYNQ platforms to deal with the issue of limited onboard resources, and an FPGA-based accelerator is designed to accelerate the inference process through pipeline, data reuse, ping-pong operation and array partition. The performance of SiamSDT was evaluated on OTB and UAV123 dataset. On the UAV123 dataset, SiamSDT achieves a 4.8% increase in precision and a 1.2% increase in success rate compared to the baseline algorithm without any increase in parameters. The hardware simulation experiments demonstrate that our deployment scheme can significantly reduce inference latency with an acceptable decrease in tracking performance. [ABSTRACT FROM AUTHOR]

Mussel farming is a thriving industry in New Zealand and is crucial to local communities. Currently, farmers keep track of their mussel floats by taking regular boat trips to the farm. This is a labour-intensive assignment. Integrating computer vision techniques into aquafarms will significantly alleviate the pressure on mussel farmers. However, tracking a large number of identical targets under various image conditions raises a considerable challenge. This paper proposes a new computer vision-based pipeline to automatically detect and track mussel floats in images. The proposed pipeline consists of three steps, i.e. float detection, float description, and float matching. In the first step, a new detector based on several image processing operators is used to detect mussel floats of all sizes in the images. Then a new descriptor is employed to provide unique identity markers to mussel floats based on the relative positions of their neighbours. Finally, float matching across adjacent frames is done by image registration. Experimental results on the images taken in Marlborough Sounds New Zealand have shown that the proposed pipeline achieves an 82.9% MOTA – 18% higher than current deep learning-based approaches – without the need for training. [ABSTRACT FROM AUTHOR]

Automatic license plate recognition (ALPR) technology has gained widespread use in many countries, including India. With the explosion in the number of vehicles plying over the roads in the past few years, automating the process of documenting vehicle license plates for use by law enforcement agencies and traffic management authorities has great significance. There have been various advancements in the object detection, object tracking, and optical character recognition domain but integrated pipelines for ALPR in Indian scenarios are a rare occurrence. This paper proposes an architecture that can track vehicles across multiple frames, detect number plates and perform optical character recognition (OCR) on them. A dataset consisting of Indian vehicles for the detection of oblique license plates is collected and a framework to increase the accuracy of OCR using the data across multiple frames is proposed. The proposed system can record license plate readings of vehicles averaging 527.99 and 2157.09 ms per frame using graphics processing unit (GPU) and central processing unit (CPU) respectively. [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.)

The Vista-Lite system labels major challenges in video surveillance involving computational complexity, restricted transferability over datasets, and the absence of an impactful approach to examine data from various cameras. This system emphasis three methodologies to solve these issues UOAL, TempoNet and BDSO. UOAL identifies abnormalities in video content via a segmentation approach improving accuracy in complex environments. TempoNet concentrates on forecasting motions and behaviors utilizing modern neural network frameworks, enhancing response times in identifying possibly malicious situations. BDSO enhances the computational resources by tuning system parameters thus assuring flexibility and decreasing false alarms. This fusion improves system persistence, sensibility and functional cost-efficiency making the solution versatile to vast surveillance scenarios. Comprehensive experiments using pedestrian, UCSD, and mall datasets established increased performance with 99% accuracy indicating the system’s capacity to maintain real-time, multi-camera data. Vista-Lite provides a novel, innovative, flexible approach to video surveillance combining anomaly detection, motion prediction, and resource optimization for improving and enhancing the domain. [ABSTRACT FROM AUTHOR]

Vehicle flow detection and tracking are crucial components of intelligent transportation systems. However, traditional methods often struggle with challenges such as the poor detection of small objects and low efficiency when processing large-scale data. To address these issues, this paper proposes a vehicle flow detection and tracking method that integrates an improved YOLOv8n model with the ByteTrack algorithm. In the detection module, we introduce the innovative MSN-YOLO model, which combines the C2f_MLCA module, the Detect_SEAM module, and the NWD loss function to enhance feature fusion and improve cross-scale information processing. These enhancements significantly boost the model's ability to detect small objects and handle complex backgrounds. In the tracking module, we incorporate the ByteTrack algorithm and train unique vehicle re-identification (Re-ID) features, ensuring robust multi-object tracking in complex environments and improving the stability and accuracy of vehicle flow tracking. The experimental results demonstrate that the proposed method achieves a mean Average Precision (mAP) of 62.8% at IoU = 0.50 and a Multiple Object Tracking Accuracy (MOTA) of 72.16% in real-time tracking. These improvements represent increases of 2.7% and 3.16%, respectively, compared to baseline algorithms. This method provides effective technical support for intelligent traffic management, traffic flow monitoring, and congestion prediction. [ABSTRACT FROM AUTHOR]

For surveillance video management in university laboratories, issues such as occlusion and low-resolution face capture often arise. Traditional face recognition algorithms are typically static and rely heavily on clear images, resulting in inaccurate recognition for low-resolution, small-sized faces. To address the challenges of occlusion and low-resolution person identification, this paper proposes a new face recognition framework by reconstructing Retinaface-Resnet and combining it with Quality-Adaptive Margin (adaface). Currently, although there are many target detection algorithms, they all require a large amount of data for training. However, datasets for low-resolution face detection are scarce, leading to poor detection performance of the models. This paper aims to solve Retinaface's weak face recognition capability in low-resolution scenarios and its potential inaccuracies in face bounding box localization when faces are at extreme angles or partially occluded. To this end, Spatial Depth-wise Separable Convolutions are introduced. Retinaface-Resnet is designed for face detection and localization, while adaface is employed to address low-resolution face recognition by using feature norm approximation to estimate image quality and applying an adaptive margin function. Additionally, a multi-object tracking algorithm is used to solve the problem of moving occlusion. Experimental results demonstrate significant improvements, achieving an accuracy of 96.12% on the WiderFace dataset and a recognition accuracy of 84.36% in practical laboratory applications. [ABSTRACT FROM AUTHOR]

Advanced Driver Assistance Systems (ADAS) rely on accurate road object detection, tracking, and distance estimation to enhance road safety. This paper presents ROD-YOLOv8, an innovative framework that leverages a monocular camera for these tasks within ADAS. By integrating the YOLOv8 model with transfer learning, the framework achieves high accuracy in road object detection. The model was retrained on the BDD100k dataset and further validated using COCO, KITTI, and PASCAL VOC datasets for comparison with existing models, achieving a mean average precision (mAP) of 0.782 and an F1 score of 0.874 on the COCO dataset. Object tracking is maintained through the Bot-Sort algorithm, ensuring consistent tracking of detected objects and providing continuous monitoring and future location prediction. Advanced camera calibration techniques enable accurate distance estimation between the camera and the detected objects, resulting in a mean absolute distance error of just 2.4 meters. Operating at an impressive 83 frames per second (FPS), ROD-YOLOv8 showcases its real-time capabilities, contributing to safer autonomous and assisted driving experiences. The proposed object detection model demonstrates improved performance compared to existing methods, particularly outperforming other methods like Mobile NET and earlier versions of YOLO in terms of precision, recall, and speed, measured in Frames Per Second (FPS), making it highly suitable for integration into ADAS applications. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology 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.)

UAVs are widely used for multiple tasks, which in many cases require autonomous processing and decision making. This autonomous function often requires significant computational capabilities that cannot be integrated into the UAV due to weight or cost limitations, making the distribution of the workload and the combination of the results produced necessary. In this paper, a dual-stage processing architecture for object detection and tracking in Unmanned Aerial Vehicles (UAVs) is presented, focusing on efficient resource utilization and real-time performance. The proposed system delegates lightweight detection tasks to onboard hardware while offloading computationally intensive processes to a ground server. The UAV is equipped with a Raspberry Pi for onboard data processing, utilizing an Intel Neural Compute Stick 2 (NCS2) for accelerated object detection. Specifically, YOLOv5n is selected as the onboard model. The UAV transmits selected frames to the ground server, which handles advanced tracking, trajectory prediction, and target repositioning using state-of-the-art deep learning models. Communication between the UAV and the server is maintained through a high-speed Wi-Fi link, with a fallback to a 4G connection when needed. The ground server, equipped with an NVIDIA A40 GPU, employs YOLOv8x for object detection and DeepSORT for multi-object tracking. The proposed architecture ensures real-time tracking with minimal latency, making it suitable for mission-critical UAV applications such as surveillance and search and rescue. The results demonstrate the system's robustness in various environments, highlighting its potential for effective object tracking under limited onboard computational resources. The system achieves recall and accuracy scores as high as 0.53 and 0.74, respectively, using the remote server, and is capable of re-identifying a significant portion of objects of interest lost by the onboard system, measured at approximately 70%. [ABSTRACT FROM AUTHOR]

The most common method for visual object tracking involves feeding an image pair comprising a template image and search region into a tracker. The tracker uses a backbone to process the information in the image pair. In pure Transformer-based frameworks, redundant information in image pairs exists throughout the tracking process and the corresponding negative tokens consume the same computational resources as the positive tokens while degrading the performance of the tracker. Therefore, we propose to solve this problem using an adaptive dynamic sampling strategy in a pure Transformer-based tracker, known as ADSTrack. ADSTrack progressively reduces irrelevant, redundant negative tokens in the search region that are not related to the tracked objectand the effect of noise generated by these tokens. The adaptive dynamic sampling strategy enhances the performance of the tracker by scoring and adaptive sampling of important tokens, and the number of tokens sampled varies according to the input image. Moreover, the adaptive dynamic sampling strategy is a parameterless token sampling strategy that does not use additional parameters. We add several extra tokens as auxiliary tokens to the backbone to further optimize the feature map. We extensively evaluate ADSTrack, achieving satisfactory results for seven test sets, including UAV123 and LaSOT. [ABSTRACT FROM AUTHOR]

Underwater computer vision plays a vital role in ocean research, enabling autonomous navigation, infrastructure inspections, and marine life monitoring. However, the underwater environment presents unique challenges, including color distortion, limited visibility, and dynamic light conditions, which hinder the performance of traditional image processing methods. Recent advancements in deep learning (DL) have demonstrated remarkable success in overcoming these challenges by enabling robust feature extraction, image enhancement, and object recognition. This review provides a comprehensive analysis of cutting-edge deep learning architectures designed for underwater object detection, segmentation, and tracking. State-of-the-art (SOTA) models, including AGW-YOLOv8, Feature-Adaptive FPN, and Dual-SAM, have shown substantial improvements in addressing occlusions, camouflaging, and small underwater object detection. For tracking tasks, transformer-based models like SiamFCA and FishTrack leverage hierarchical attention mechanisms and convolutional neural networks (CNNs) to achieve high accuracy and robustness in dynamic underwater environments. Beyond optical imaging, this review explores alternative modalities such as sonar, hyperspectral imaging, and event-based vision, which provide complementary data to enhance underwater vision systems. These approaches improve performance under challenging conditions, enabling richer and more informative scene interpretation. Promising future directions are also discussed, emphasizing the need for domain adaptation techniques to improve generalizability, lightweight architectures for real-time performance, and multi-modal data fusion to enhance interpretability and robustness. By critically evaluating current methodologies and highlighting gaps, this review provides insights for advancing underwater computer vision systems to support ocean exploration, ecological conservation, and disaster management.

Urban traffic management has been facing increasing challenges due to the surge in number of vehicles and traffic congestion. As cities expand and population grows, efficient and accurate monitoring of vehicle counts is crucial for better traffic management. Hence, as part of the Bengaluru Mobility Challenge 2024, organized by Bengaluru Traffic Police in collaboration with The Indian Institute of Science, we propose a solution to address the issue by developing a predictive model to estimate vehicle counts by turning pattern from traffic video footage. The dataset consists of traffic video footage of 23 different junctions around Bengaluru, on which 7 vehicle classes had to be detected, namely, Car, Truck, Bus, Two-Wheeler, Three-Wheeler, Light Commercial Vehicle and Bicycle. The proposed work focuses on two key objectives: counting vehicle turns over 30-minute clips and forecasting future vehicle turn counts by class for the next 30 minutes. A You Only Look Once (YOLOv8) and Auto-ARIMA based pipeline was deployed to address the challenge, which demonstrated robust detection capabilities, with an overall precision of 92.59% for vehicle detection. Building on this, we designed a custom vehicle counting algorithm that integrated the BoT-SORT tracker with dynamic counting boxes, accurately capturing vehicle movements and turn patterns in real-time and this integrated approach attained a best deviation of 20.79%. for turn pattern counting and 28.41% for forecasting. Furthermore, the system is scalable to accommodate any number of cameras and is capable of forecasting traffic over extended time frames, allowing it to be applied to a variety of urban traffic monitoring scenarios. These results highlight the effectiveness of our custom designed framework in real-world scenarios as a reliable model for applications needing high-precision detection and predictive analytics.