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Crowd counting aims to estimate the number and distribution of the population in crowded places, which is an important research direction in object counting. It is widely used in public place management, crowd behavior analysis, and other scenarios, showing its robust practicality. In recent years, crowd-counting technology has been developing rapidly. However, in highly crowded and noisy scenes, the counting effect of most models is still seriously affected by the distortion of view angle, dense occlusion, and inconsistent crowd distribution. Perspective distortion causes crowds to appear in different sizes and shapes in the image, and dense occlusion and inconsistent crowd distributions result in parts of the crowd not being captured completely. This ultimately results in the imperfect capture of spatial information in the model. To solve such problems, we propose a strip pooling combined attention (SPCANet) network model based on normed-deformable convolution (NDConv). We model long-distance dependencies more efficiently by introducing strip pooling. In contrast to traditional square kernel pooling, strip pooling uses long and narrow kernels (1×N or N×1) to deal with dense crowds, mutual occlusion, and overlap. Efficient channel attention (ECA), a mechanism for learning channel attention using a local cross-channel interaction strategy, is also introduced in SPCANet. This module generates channel attention through a fast 1D convolution to reduce model complexity while improving performance as much as possible. Four mainstream datasets, Shanghai Tech Part A, Shanghai Tech Part B, UCF-QNRF, and UCF CC 50, were utilized in extensive experiments, and mean absolute error (MAE) exceeds the baseline, which is 60.9, 7.3, 90.8, and 161.1, validating the effectiveness of SPCANet. Meanwhile, mean squared error (MSE) decreases by 5.7% on average over the four datasets, and the robustness is greatly improved.

Crowd counting in aerial images presents unique challenges due to varying altitudes, angles, and cluttered backgrounds. Additionally, the small size of targets, often occupying only a few pixels in high-resolution images, further complicates the problem. Current crowd counting models struggle in these complex scenarios, leading to inaccurate counts, which are crucial for crowd management. Moreover, these regression-based models only provide the total count without indicating the location or distribution of people within the environment, limiting their practical utility. While YOLOv8 has achieved significant success in detecting small targets within aerial imagery, it faces challenges when directly applied to crowd counting tasks in such contexts. To overcome these challenges, we propose an improved framework based on YOLOv8, incorporating a context enrichment module (CEM) to capture multiscale contextual information. This enhancement improves the model’s ability to detect and localize tiny targets in complex aerial images. We assess the effectiveness of the proposed framework on the challenging VisDrone-CC2021 dataset, and our experimental results demonstrate the effectiveness of this approach.

Crowd counting methods have become increasingly mature. However, the problem of dramatic scale variation still exists. For this reason, we propose an efficient separated and fused pyramid attention network, which can extract multiscale features on channels and space and greatly alleviate the problem of dramatic scale variation. First, in order to extract the rich features on the channel, we design a separated and fused channel attention module, which is composed of two 3x3 convolution layers, a separated attention module, and a SE module. Second, we design a spatial contextual feature fusion module to fully extract multiscale features in spatial dimensions. Finally, we conduct comparison experiments with state-of-the-art methods on several challenging datasets, including the ShanghaiTech, UCF_CC_50, and WorldExpo'10 datasets. The experimental results show our method outperforms most of the state-of-the-art methods. We conduct ablation experiments on the ShanghaiTech Part A and Part B datasets to verify the importance of each submodule. [ABSTRACT FROM AUTHOR]

Efficient feature representation is the key to improving crowd counting performance. CNN and Transformer are the two commonly used feature extraction frameworks in the field of crowd counting. CNN excels at hierarchically extracting local features to obtain a multi-scale feature representation of the image, but it struggles with capturing global features. Transformer, on the other hand, could capture global feature representation by utilizing cascaded self-attention to capture remote dependency relationships, but it often overlooks local detail information. Therefore, relying solely on CNN or Transformer for crowd counting has certain limitations. In this paper, we propose the TCHNet crowd counting model by combining the CNN and Transformer frameworks. The model employs the CMT (CNNs Meet Vision Transformers) backbone network as the Feature Extraction Module (FEM) to hierarchically extract local and global features of the crowd using a combination of convolution and self-attention mechanisms. To obtain more comprehensive spatial local information, an improved Progressive Multi-scale Learning Process (PMLP) is introduced into the FEM, guiding the network to learn at different granularity levels. The features from these three different granularity levels are then fed into the Multi-scale Feature Aggregation Module (MFAM) for fusion. Finally, a Multi-Scale Regression Module (MSRM) is designed to handle the multi-scale fused features, resulting in crowd features rich in high-level semantics and low-level detail. Experimental results on five benchmark datasets demonstrate that TCHNet achieves highly competitive performance compared to some popular crowd counting methods. [ABSTRACT FROM AUTHOR]

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As a challenging issue in computer vision, crowd counting has been increasingly studied. A convolutional neural network (CNN) is an effective system for handling crowd counting, based on constructing a CNN to generate a high-quality density estimation map. However, conventional CNN-based methods only consider the mapping from the crowd image to the density map, neglecting reconstruction from the density map to the crowd image and the impact of this reconstruction on the CNN performance. Here, we present a novel model denoted a dual-CNN (DualCNN) to improve the conventional CNN performance on crowd counting. Our DualCNN comprises a primal network for generating the density maps from the crowd image and a secondary network for reconstructing the crowd image from the density map. The two networks are trained through an iterative and alternating learning process, and the performance of the final model is improved by considering the interactions of the two networks. In addition, we introduce the attention mechanism into the dual network to enhance the primal network robustness against the background influence of the crowd image. The experimental results indicate that the proposed method significantly improves the performance of CNNs in crowd counting. [ABSTRACT FROM AUTHOR]

Crowd counting is an important task that serves as a preprocessing step in many applications. Despite obvious improvement reported by various convolutional-neural-network-based approaches, they only focus on the role of deep feature maps while neglecting the importance of shallow features for crowd counting. In order to surmount this issue, a dilated convolutional-neural-network-based cross-level contextual information extraction network is proposed in this work, which is abbreviated as CL-DCNN. Specifically, a dilated contextual module (DCM) is constructed by importing cross-level connection between different feature maps. It can effectively integrate contextual information while conserving the local details of crowd scenes. Extensive experiments show that the proposed approach outperforms state-of-the-art approaches using five public datasets, i.e., ShanghaiTech part A, ShanghaiTech part B, Mall, UCF_CC_50 and UCF-QNRF, achieving MAE 52.6, 8.1, 1.55, 181.8, and 96.4, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep feature network with multi-scale fusion (DFNet) for addressing the problem of crowd counting in highly congested noisy scenes. DFNet contains three modules: feature encoder, feature decoder and feature fusion. The feature encoder uses a VGG-16-based convolutional neural network (CNN) that encodes features from images and forms a kind of low-level spatial information. The feature decoder is a multi-column dilated convolutional neural network (McDCNN) with different dilation rates that can capture a multi-scale contextual information, decode the low-level spatial information and generate a kind of high-level semantic information. Furthermore, the multi-column architecture in McDCNN can effectively relieve the "gridding" issue presented in the dilated convolution framework. The feature fusion block uses a simple and effective network architecture to sufficiently incorporate the low-level spatial and the high-level semantic information for facilitating high-quality density map estimation and performing accurate crowd counting. Extensive experiments on several highly challenging crowd counting datasets are conducted. Experimental results show that DFNet is comparable with recent state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Crowd counting (CC) and density estimation are crucial for ensuring public safety and security in surveillance videos with large audiences. As computer vision-based scene interpretation advances, automatic analysis of crowd situations is becoming increasingly prevalent. However, existing crowd analysis algorithms may not accurately interpret the video footage. To address this challenge, we propose a new approach called SMOHDL-CCA. This approach combines a Slime Mold Optimization algorithm with a Hybrid Deep Learning Enabled CC Approach. Our system uses the SMO algorithm with an optimized neural network search network (NASNet) model as the front-end to take advantage of transfer learning and flexible characteristics. The back-end model employs Dilated Convolutional Neural Networks, and the hyperparameter tuning process is done using the Chicken Swarm Optimization algorithm. Given a crowded video input frame, our SMOHDL-CCA model estimates the density map of the image. Each pixel value indicates the crowd density at the corresponding location in the picture. The final crowd count is obtained by summing all the values in the density map. We evaluated our proposed approach using three standard datasets. Furthermore, the state-of-the-art combining the proposed SMOHDL-CCA model achieves comparable performance such as improved precision is 96.97%, recall is 96.94%, and F1 score is 96.61%, reduced mean squared error of 61.15 values for the NWPU-crowd, UCF_QNRF, and World Expo datasets. [ABSTRACT FROM AUTHOR]

Crowd-counting networks have become the mainstream method to deploy crowd-counting techniques on resource-constrained devices. Significant progress has been made in this field, with many outstanding lightweight models being proposed successively. However, challenges like scare-variation, global feature extraction, and fine-grained head annotation requirements still exist in relevant tasks, necessitating further improvement. In this article, we propose a weakly supervised hybrid lightweight crowd-counting network that integrates the initial layers of GhostNet as the backbone to efficiently extract local features and enrich intermediate features. The incorporation of a modified Swin-Transformer block addresses the need for effective global context information. A Pyramid Pooling Aggregation Module handles the inherent scale variation problem in crowd-counting tasks in a more computation-efficient way. This module, along with the cross-attention module, serves as bridges to promote the feature information flow between local features and global context information. Finally, a simplified regressor module is designed to allow the proposed model with weakly supervised guidance for training to avoid precise location-level annotations, noting that the omission of density map generation makes the proposed network more lightweight. Our results on the UCF-QNRF dataset indicate our model is 8.73% and 12.17% more accurate on MAE and MSE metrics, respectively, than the second-best ARNet, in which the parameters decrease by 4.52%. On the ShanghaiTech A dataset, MAE and MSE drop 1.5% and 3.2%, respectively, compared to the second-best PDDNet. The experimental results for accuracy and inference speed evaluation on some mainstream datasets validate the effective design principle of our model. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) provides a collaborative infrastructure to communicate smart devices with cloud-edge healthcare applications, medical devices, wearable biosensors, etc. On the other hand, crowd counting as one of computer vision approaches is an emerging topic to detect any objects with static or dynamic mobility in the IoT environments. Smart crowd counting enables pattern recognition for many intelligent applications such as microbiology, surveillance, healthcare systems, crowdedness estimation, and other environmental case studies. According to complicated capturing systems in the IoT environments, crowd counting methods can influence on performance of object detection in the critical case studies using Artificial Intelligence (AI)-based approaches such as machine learning, deep learning, collaborative learning, fuzzy logic and meta-heuristic algorithms. This paper provides a new comprehensive technical analysis for existing AI-based crowd counting approaches in healthcare and medical systems, biotechnology and IoT environments. Meanwhile, it presents a discussion on the existing case studies with respect to analyzing technical aspects and applied algorithms to enhance pattern prediction factors. Finally, some new innovative efforts and challenges are presented for new research upcoming and open issues. [ABSTRACT FROM AUTHOR]

Recent progress in crowd counting and localization methods mainly relies on expensive point-level annotations and convolutional neural networks with limited receptive filed, which hinders their applications in complex real-world scenes. To this end, we present CLFormer, a Transformer-based weakly supervised crowd counting and localization framework. The model extracts global information from the input image using a Transformer and then passes the extracted features to both a regression branch for crowd counting and a localization branch for localization. Initial proposals are produced by the localization branch and filtered via score maps generated from the extracted features, and their centers are used as pseudo-point-level annotations. Through staggered training of the two branches, the quality of pseudo-point-level annotations is improved, and the final localization maps are generated. Experiments on four benchmark datasets (i.e., ShanghaiTech, UCF-QNRF, JHU-CROWD++, and NWPU-Crowd) demonstrate that CLFormer obtains better counting performance than weakly supervised and fully supervised counting networks and comparable localization performance to fully supervised localization networks. [ABSTRACT FROM AUTHOR]

Copyright of Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University 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.)

Crowd counting and density estimation are the principal objectives of crowd analysis, which offer significant applications in surveillance, event management, and traffic design. In the field of crowd flow, including simulations, the dynamics of crowd movement exhibit characteristics such as followability and, thus, are categorized under a distinct flow paradigm. The recent advancements in deep learning have propelled the usage of neural networks tailored for crowd counting and density estimation from video feeds. Nonetheless, prior models did not consider crowd dynamics. This study proposes a novel method that combines neural networks with crowd dynamics. Specifically, we introduced a new penalty term that represents prior knowledge of crowd dynamics and refined the neural network outputs via static/dynamic floor field models, and grid-based crowd dynamics models. Empirical evaluation on benchmark datasets demonstrated the superiority of the proposed method over existing state-of-the-art techniques. Further analysis of each scene confirmed that the crowd counting performance is highly dependent on the scene, and the impact of the three methodological components (i.e., the penalty term and the two-floor fields) on performance varies across scenes. In particular, the floor-field model tended to be more effective when there were no significant changes in the scene. Our code is available on GitHub. https://github.com/hanebarla/ FF-guided-NeuralCC

In this paper, we propose a novel and enhanced approach for crowd counting within the domain of manatee monitoring, aiming to significantly improve efficiency and accuracy. The proposed model achieves state-of-the-art results in the challenging task of manatee counting, simplifying the work of scientists and experts in the field. Our model not only facilitates the identification and enumeration of manatees in images and videos but also excels in scenarios that pose considerable challenges for human observers. To enhance accurate counting of the manatee aggregation, we introduce a framework with three key innovations to tackle the challenge: a new approach to generate density maps during the training process, an augmented technique to balance the dataset, and a cross-domain solution to enhance overall performance. The proposed two-dimensional Gaussian kernel offers a refined method for creating density maps, providing a more robust foundation for the training phase. Additionally, we built a balanced and augmented dataset, ensuring that the model is exposed to diverse and representative instances, thus improving its generalization capabilities. Furthermore, we incorporate a cross-domain phase pretraining the model utilizing an image dataset of wild animals to initialize the weights and further improve performance. Experiments and comparisons, with respect to previously established CSRNET model presented in Wang et al. (2023), demonstrate noteworthy improvements. Remarkably, our model achieves a Mean Absolute Error (MAE) of nearly half compared to the rival approach, showcasing the substantial advancements achieved through our refined methodology. This progress boosts the reliability of manatee counting in conservation efforts and ecological research.

Recent work in crowd counting focuses on counting over detected individuals rather than estimating the number of people in the image. However, existing crowd localization methods directly detect the head point or region of individuals, which may entail non-responsibility of the outputs that fall outside the grid. Our proposed Pseudo Square Label Network (PSL-Net) presents a novel method for crowd counting and localization, which takes advantage of the anchor-free detection in which PSL-Net predicts the probability of the center point that fall into the responsible grid, while indirectly detecting an individual outside of the responsible grid through box regression and centerness estimation. This study proposes to supervise with pseudo square label(PSL), which is generated around point annotation with fixed size. Furthermore, we design a partial many-to-one matching algorithm to assign precise labels by only matching within PSL during the training phase, and associate the predicted points with their responsible grids through centerness during the inference phase. As a result, not only PSL-Net achieves state-of-the-art on ShanghaiTech Part A and B, which are the most popular datasets in crowd counting, but also achieves state-of-the-art among the point detection-based methods in crowd localization.

Accurately measuring the level of crowding in transit cars is crucial for ensuring passenger safety and efficient operation. However, applying object detection algorithms to crowd counting in transit cars poses difficulties due to the low viewpoint of the cameras and the labor-intensive task of image labeling. Although some researchers have explored regression-based crowd counting methods without labeling with bounding boxes, their approaches still necessitate manual counting of passengers for image labeling. To overcome these challenges, we propose a novel calibration method for regression-based models that minimizes the number of labeled images required for training. Our approach employs image pairs and triplets with ranks for reinforcing the model training. Subsequently, the training task requires a minimal number of images labeled with exact passenger counts. Experimental results demonstrate that our proposed calibration approach considerably enhances the crowd counting performance of the conventional regression-based model. Specifically, our method reduces the mean absolute error (MAE) by 76.5% and 34.3% for conventional detection- and regression-based calibration methods, respectively.

Dense urban environments pose significant challenges when it comes to detecting and measuring crowd size due to their nature of being free-flow environments containing many dynamic factors. In this paper, we use a wireless sensor network (WSN) to perform device-free crowd size estimation in a subway station. Our sensing solution uses the change in attenuation of the communication links between sensor nodes to estimate the number of people standing on the platform. In order to achieve this, we use the same attenuation information coming from the WSN to detect the presence of a rail vehicle in the station and compensate for the channel fading caused by the introduced rail vehicle. We make use of two separately trained regression models depending on the presence or absence of a rail vehicle to estimate the people count. The detection of rail vehicles occurred with a near-perfect accuracy. When evaluating the resulting estimation model on our test set, we achieved a mean average error of 3.567 people, which is a significant improvement over 6.192 people when using a single regression model. This demonstrates that device-free sensing technologies can be successfully implemented in dynamic environments by implementing detection techniques and using different regression models depending on the environment’s state.

This study focuses on the problem of dense object counting. In dense scenes, variations in object scales and uneven distributions greatly hinder counting accuracy. The current methods, whether CNNs with fixed convolutional kernel sizes or Transformers with fixed attention sizes, struggle to handle such variability effectively. Lower-resolution features are more sensitive to larger objects closer to the camera, while higher-resolution features are more efficient for smaller objects further away. Thus, preserving features that carry the most relevant information at each scale is crucial for improving counting precision. Motivated by this, we propose a multi-resolution scale feature fusion-based universal density counting network (MRSNet). It utilizes independent modules to process high- and low-resolution features, adaptively adjusts receptive field sizes, and incorporates dynamic sparse attention mechanisms to optimize feature information at each resolution, by integrating optimal features across multiple scales into density maps for counting evaluation. Our proposed network effectively mitigates issues caused by large variations in object scales, thereby enhancing counting accuracy. Furthermore, extensive quantitative analyses on six public datasets demonstrate the algorithm’s strong generalization ability in handling diverse object scale variations.

Abstract Passive crowd counting using channel state information (CSI) is a promising technology for applications in fields such as smart cities and commerce. However, the most existing algorithms can only recognize the total number of people in the monitoring area and cannot simultaneously recognize the number and states of people and ignore the real-time performance of the algorithms. Therefore, they cannot be applied to the scenarios of multi-state crowd counting requiring high real-time performance. To address this issue, a lightweight passive multi-state crowd counting algorithm called TF-LPMCC is proposed. This algorithm constructs CSI amplitude data into amplitude and time–frequency images, extracts texture features using the gray-level co-occurrence matrix (GLCM) and gray-level difference statistic (GLDS) methods, and uses the linear discriminant analysis (LDA) algorithm to count the crowd in multi-states. Experiments show that the TF-LPMCC algorithm not only has low time complexity but also achieves an average recognition accuracy of 98.27% for crowd counting.

Most researchers focus on designing accurate crowd counting models with heavy parameters and computations but ignore the resource burden during the model deployment. A real-world scenario demands an efficient counting model with low-latency and high-performance. Knowledge distillation provides an elegant way to transfer knowledge from a complicated teacher model to a compact student model while maintaining accuracy. However, the student model receives the wrong guidance with the supervision of the teacher model due to the inaccurate information understood by the teacher in some cases. In this paper, we propose a dual-knowledge distillation (DKD) framework, which aims to reduce the side effects of the teacher model and transfer hierarchical knowledge to obtain a more efficient counting model. First, the student model is initialized with global information transferred by the teacher model via adaptive perspectives. Then, the self-knowledge distillation forces the student model to learn the knowledge by itself, based on intermediate feature maps and target map. Specifically, the optimal transport distance is utilized to measure the difference of feature maps between the teacher and the student to perform the distribution alignment of the counting area. Extensive experiments are conducted on four challenging datasets, demonstrating the superiority of DKD. When there are only approximately 6% of the parameters and computations from the original models, the student model achieves a faster and more accurate counting performance as the teacher model even surpasses it. [ABSTRACT FROM AUTHOR]

Crowd counting aims to estimate the number, density, and distribution of crowds in an image. The current mainstream approach, based on CNN, has been highly successful. However, CNN is not without its flaws. Its limited receptive field hampers the modeling of global contextual information, and it struggles to effectively handle scale variation and background complexity. In this paper, we propose a Multi-scale Hybrid Attention Network called MHANet to solve crowd counting challenges more effectively. To address the issue of scale variation, we have developed a Multi-scale Aware Module (MAM) that incorporates multiple sets of dilated convolutions with varying dilation rates. The MAM significantly improves the network's ability to extract information at multiple scales. To tackle the problem of background complexity, we have introduced a Hybrid Attention Module (HAM) that combines spatial attention and channel attention. The HAM effectively directs attention to the crowd region while suppressing background interference, resulting in more accurate counting. MHANet has been extensively experimented on four benchmark datasets and compared against state-of-the-art algorithms. It consistently achieves superior performance in terms of the MAE evaluation metric. MHANet outperforms the current state-of-the-art methods by margins of 1.9%, 5.4%, 0.4%, and 0.8% on the ShanghaiTech Part_A, ShanghaiTech Part_B, UCF-QNRF, and UCF_CC_50 datasets, respectively. Furthermore, a series of ablation experiments targeting MAM and HAM were conducted in this paper, and the experimental results fully demonstrate that MAM and HAM can effectively address the challenges of scale variation and background complexity, ultimately enhancing the accuracy and robustness of the network. [ABSTRACT FROM AUTHOR]

Crowd counting technology is to let people understand the spatial distribution of crowds in various scenes. In reality, a large number of occlusions and scale variations make it extremely challenging to achieve accurate counting in crowded venues. Aiming at these problems, this paper designs a crowd density estimation network that can maintain good accuracy in scenes that are both crowded and have large-scale changes: Texture Feature Attention Convolutional Neural Network (TFA-CNN). Specifically: (1) A Differential Texture Module (DT Module) is proposed to identify various texture features of the bottom feature map and to better distinguish between background and foreground regions; (2) proposed the Multi-Channel Threshold Replacement Attention Module (MTRA Module), which combines channel and spatial attention mechanisms to allow the network to pay more focus on the head position of the crowd, thereby reducing the counting error. TFA-CNN has conducted multiple experiments on several publicly available and challenging datasets, and the results are superior to many SOTA methods, demonstrating excellent generalization and robustness. [ABSTRACT FROM AUTHOR]

Crowd counting provides an important foundation for public security and urban management. Due to the existence of small targets and large density variations in crowd images, crowd counting is a challenging task. Mainstream methods usually apply convolution neural networks (CNNs) to regress a density map, which requires annotations of individual persons and counts. Weakly-supervised methods can avoid detailed labeling and only require counts as annotations of images, but existing methods fail to achieve satisfactory performance because a global perspective field and multi-level information are usually ignored. We propose a weakly-supervised method, DTCC, which effectively combines multi-level dilated convolution and transformer methods to realize end-to-end crowd counting. Its main components include a recursive swin transformer and a multi-level dilated convolution regression head. The recursive swin transformer combines a pyramid visual transformer with a fine-tuned recursive pyramid structure to capture deep multi-level crowd features, including global features. The multi-level dilated convolution regression head includes multi-level dilated convolution and a linear regression head for the feature extraction module. This module can capture both low- and high-level features simultaneously to enhance the receptive field. In addition, two regression head fusion mechanisms realize dynamic and mean fusion counting. Experiments on four well-known benchmark crowd counting datasets (UCF_CC_50, ShanghaiTech, UCF_QNRF, and JHU-Crowd++) show that DTCC achieves results superior to other weakly-supervised methods and comparable to fully-supervised methods. [ABSTRACT FROM AUTHOR]

Crowd behavior prediction (CBP) and crowd counting (CC) are the essential functions of vision-based crowd analysis (CA), which play a crucial role in controlling crowd disasters. The CA using different models for the CBP and the CC will increase computational overheads and have synchronization issues. The state-of-the-art approaches utilized deep convolutional architectures to exploit spatial-temporal features to accomplish the objective, but such models suffer from computational complexities during convolution operations. Thus, to sort out the issues as mentioned earlier, this paper develops a single deep model which performs two functionalities of CA: CBP and CC. The proposed model uses multilayers of depth-wise separable CNN (DSCNN) to extract fine-grained spatial-temporal features from the scene. The DSCNN can minimize the number of matrix multiplications during convolution operation compared to traditional CNN. Further, the existing datasets are available to accomplish the single functionality of CA. In contrast, the proposed model needs a dual-tasking CA dataset which should provide the ground-truth labels for CBP and CC. Thus, a dual functionality CA dataset is prepared using a benchmark crowd behavior dataset, i.e. MED. Around 41000 frames have been manually annotated to obtain ground-truth crowd count values. This paper also demonstrates an experiment on the proposed multi-functional dataset and outperforms the state-of-the-art methods regarding several performance metrics. In addition, the proposed model processes each test frame at 3.40 milliseconds, and thus is easily applicable in real-time. [ABSTRACT FROM AUTHOR]

Traditional approach of mobile crowd estimation involves counting a group of individuals at a specific place, manually, in real-time. It is a laborious exercise that can be physically and mentally demanding. In Hong Kong, a large rally can last more than six hours, making the manual count method susceptible to human errors. While crowd counting using object detection and tracking has been well-established in computer vision, such application has remained relatively small scale within a controlled indoor setting (e.g. counting people at fixed gateways in a mall). No attempt to date has applied the automatic crowd counting method to count hundreds of thousands of people along an open stretch of rally route within the complex urban outdoor landscape. This research proposed an integrated approach that combines the capture-recapture method in statistics and a Convolutional Neural Network (CNN) method in computer vision to count the mobile crowd. The research teams implemented the integrative approach and counted 276,970 people with a 95% confidence interval of 263,663 to 290,276 in the 2019, July 1st Rally in Hong Kong. This work counted the attendance of a large-scale rally as a proof of concept to fill in a gap in the empirical studies. The intellectual merits and research findings shed useful insights to improve mobile population estimation and leverage alternative data sources to support related scientific applications. [ABSTRACT FROM AUTHOR]

In this study, we address the class-agnostic counting (CAC) challenge, aiming to count instances in a query image, using just a few exemplars. Recent research has shifted towards few-shot counting (FSC), which involves counting previously unseen object classes. We present ACECount, an FSC framework that combines attention mechanisms and convolutional neural networks (CNNs). ACECount identifies query image–exemplar similarities, using cross-attention mechanisms, enhances feature representations with a feature attention module, and employs a multi-scale regression head, to handle scale variations in CAC. ACECount's experiments on theFSC-147 dataset exhibited the expected performance. ACECount achieved a reduction of 0.3 in the mean absolute error (MAE) on the validation set and a reduction of 0.26 on the test set of FSC-147, compared to previous methods. Notably, ACECount also demonstrated convincing performance in class-specific counting (CSC) tasks. Evaluation on crowd and vehicle counting datasets revealed that ACECount surpasses FSC algorithms like GMN, FamNet, SAFECount, LOCA, and SPDCN, in terms of performance. These results highlight the robust dataset generalization capabilities of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Weakly supervised crowd counting involves the regression of the number of individuals present in an image, using only the total number as the label. However, this task is plagued by two primary challenges: the large variation of head size and uneven distribution of crowd density. To address these issues, we propose a novel Hypergraph Association Crowd Counting (HACC) framework. Our approach consists of a new multi-scale dilated pyramid module that can efficiently handle the large variation of head size. Further,we propose a novel hypergraph association module to solve the problem of uneven distribution of crowd density by encoding higher-order associations among features, which opens a new direction to solve this problem. Experimental results on multiple datasets demonstrate that our HACC model achieves new state-of-the-art results. [ABSTRACT FROM AUTHOR]

Automated crowd counting is a crucial aspect of surveillance, especially in the context of mass events attended by large populations. Traditional methods of manually counting the people attending an event are error-prone, necessitating the development of automated methods. Accurately estimating crowd counts across diverse scenes is challenging due to high variations in the sizes of human heads. Regression-based crowd-counting methods often overestimate counts in low-density situations, while detection-based models struggle in high-density scenarios to precisely detect the head. In this work, we propose a unified framework that integrates regression and detection models to estimate the crowd count in diverse scenes. Our approach leverages a routing strategy based on crowd density variations within an image. By classifying image patches into density levels and employing a Patch-Routing Module (PRM) for routing, the framework directs patches to either the Detection or Regression Network to estimate the crowd count. The proposed framework demonstrates superior performance across various datasets, showcasing its effectiveness in handling diverse scenes. By effectively integrating regression and detection models, our approach offers a comprehensive solution for accurate crowd counting in scenarios ranging from low-density to high-density situations.

Prevailing crowd counting approaches primarily rely on density map regression methods. Despite wonderful progress, significant scale variations and complex background interference within the same image remain challenges. To address these issues, in this paper we propose a novel DETR-based crowd counting framework called Crowd Counting DETR (CC-DETR), which aims to extend the state-of-the-art DETR object detection framework to the crowd counting task. In CC-DETR, a DETR-like encoder–decoder structure (Hybrid Context DETR, i.e., HCDETR) is proposed to tackle complex visual information by fusing features from hybrid semantic levels through a transformer. In addition, we design a Coordinate Dilated Convolution Module (CDCM) to effectively employ position-sensitive context information in different scales. Extensive experiments on three challenging crowd counting datasets (ShanghaiTech, UCF-QNRF, and NWPU) demonstrate that our model is effective and competitive when compared against SOTA crowd counting models.