Network For Crowd Counting Research Articles

Progressive Crowd Enhancement De-Background Network for crowd counting

Progressive Crowd Enhancement De-Background Network for crowd counting

TinyCount: an efficient crowd counting network for intelligent surveillance

TinyCount: an efficient crowd counting network for intelligent surveillance

SDANet: scale-deformation awareness network for crowd counting

SDANet: scale-deformation awareness network for crowd counting

CrowdUNet: Segmentation assisted U-shaped crowd counting network

With the end of the COVID-19 pandemic, the number of pedestrians in various public places has increased dramatically. Estimating the size and density distribution of crowds accurately from images is essential for public safety. At present, there are still many factors that limit the accuracy of dense crowd counting, such as perspective distortion, background clutter and heavy occlusion. To be capable of accurately estimating the crowd size in RGB images, we propose a crowd counting network called CrowdUNet, which is assisted by a segmentation task. It applies the segmentation results to the crowd counts, making the network more focused on the prediction of foreground regions. We combine Swin Transformer Block and CNN to build a Swin Transformer Convolution(STC) module to extract deep semantic features. We analyze the characteristics of crowd images and propose a novel decoder structure called Coordinate Decoder(CD), which better aggregates low and high level features and improve the robustness of the network. In order to obtain accurate regression results, we also propose a regression head with multi-scale receptive fields, which is called Spatial Pyramid Convolution (SPC). Extensive experiments on four challenging crowd counting datasets namely ShanghaiTech A, ShanghaiTech B, UCF p=CC 50, and UCF-QNRF have validated the proposed method.

Multi-branch progressive embedding network for crowd counting

Crowd counting is essential for video surveillance and public safety. The performance of counting models has been greatly improved with the rapid development of Convolution neural networks (CNN), while it still suffers interference from complex background and large-scale variation. To relieve the above challenges, this paper proposes a novel Multi-branch Progressive Embedding Network (MPENet) for crowd counting. Specifically, the proposed network mainly consists of two modules named Background Area Filter (BAF) and Sequential Multi-scale Modules (SMM), which are embedded with each other to generate higher-quality density maps. Firstly, the BAF model base on attention mechanism is proposed to distinguish crowd from background, which effectively avoids the model outputting positive predictions in the background region. Meanwhile, a multi-level supervision mechanism is proposed to generate more accurate attention maps. Besides, the SMM module is designed to be progressively embedded with scale context information so that the scale feature will be smooth and continuous. Finally, a novel multi-scale consistency structural loss is proposed to avoid pixel-level isolation due to Euclidean loss. The proposed method significantly improves counting accuracy, achieving Mean Absolute Error (MAE) of 57.6 and 6.9 on ShanghaiTechA and ShanghaiTechB respectively.

A multi-scale fusion and dual attention network for crowd counting

A multi-scale fusion and dual attention network for crowd counting

Transformer-CNN hybrid network for crowd counting

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.

An encoder-decoder network for crowd counting based on multi-scale attention mechanism

An encoder-decoder network for crowd counting based on multi-scale attention mechanism

Double multi-scale feature fusion network for crowd counting

Double multi-scale feature fusion network for crowd counting

A Weakly Supervised Hybrid Lightweight Network for Efficient Crowd Counting

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.

JMFEEL-Net: a joint multi-scale feature enhancement and lightweight transformer network for crowd counting

JMFEEL-Net: a joint multi-scale feature enhancement and lightweight transformer network for crowd counting

Improving MLP-Based Weakly Supervised Crowd-Counting Network via Scale Reasoning and Ranking

MLP-based weakly supervised crowd counting approaches have made significant advancements over the past few years. However, owing to the limited datasets, the current MLP-based methods do not consider the problem of region-to-region dependency in the image. For this, we propose a weakly supervised method termed SR2. SR2 consists of three parts: scale-reasoning module, scale-ranking module, and regression branch. In particular, the scale-reasoning module extracts and fuses the region-to-region dependency in the image and multiple scale feature, then sends the fused features to the regression branch to obtain estimated counts; the scale-ranking module is used to understand the internal information of the image better and expand the datasets efficiently, which will help to improve the accuracy of the estimated counts in the regression branch. We conducted extensive experiments on four benchmark datasets. The final results showed that our approach has better and higher competing counting performance with respect to other weakly supervised counting networks and with respect to some popular fully supervised counting networks.

Multi-branch Segmentation-guided Attention Network for crowd counting

Crowd counting has gained increasing attention recently owing to its importance in public safety. However, it remains a challenging task due to background complexities and high annotation costs. To address these challenges, we propose the Multi-branch Segmentation-guided Attention Network (MSGANet). To deal with the complex background, we incorporate segmentation-guided attention branches into both shallow and deep layers of the baseline model, allowing simultaneous consideration of spatial and semantic information. Multi-level attention maps enable the network to minimize the influence of complex backgrounds while focusing on the regions containing crowds. To tackle the issue of costly annotations, MSGANet utilizes only point annotations to generate ground truth density and segmentation maps, eliminating additional expenses. Our results demonstrate that our approach achieves highly competitive performance compared to state-of-the-art crowd counting methods.

SFPANet: Separation and fusion pyramid attention network for crowd counting

SFPANet: Separation and fusion pyramid attention network for crowd counting

Dual convolutional neural network for crowd counting

Dual convolutional neural network for crowd counting

MHANet: Multi-scale hybrid attention network for crowd counting

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.

Deformable channel non‐local network for crowd counting

AbstractBoth global dependency and local correlation are crucial for solving the scale variation of crowd. However, most of previous methods fail to take two factors into consideration simultaneously. Against the aforementioned issue, a deformable channel non‐local network, abbreviated as DCNLNet for crowd counting, which can simultaneously learn global context information and adaptive local receptive field is proposed. Specifically, the proposed DCNLNet consists of two well‐crafted designed modules: deformable channel non‐local block (DCNL) and spatial attention feature fusion block (SAFF). The DCNL encodes long‐range dependencies between pixels and the adaptive local correlation with channel non‐local and deformable convolution, respectively, benefiting for improving the spatial discrimination of features. While the SAFF aims to aggregate the cross‐level information, which interacts these features from different depths and learns specific weights for the feature maps with spatial attention. Extensive experiments are performed on three crowd counting benchmark datasets and experimental results indicate that the proposed DCNLNet achieves compelling performance compared to other representative counting models.

Context Attention Fusion Network for crowd counting

Context information, which plays a crucial role in many computer vision tasks, can benefit deep networks to construct better cognitive competence from more comprehensive surrounding. However, most existing crowd counting methods often overlook the importance of context information extracted from both global and local views. To address or alleviate this problem, this paper proposes a novel Context Attention Fusion Network, which is abbreviated as CAFNet for crowd counting. The core idea behind CAFNet is the interaction of multiple context information, including local context, cross-level context, and cross-layer context. To explore local context, we design a local context aggregation module to extract hierarchically local semantic information and then integrate them adaptively. To utilize cross-level context, a guidance attention fusion module is designed to fuse low-level feature map as the guidance of high-level context information so that the spatial details can be effectively compensated. To make full use of cross-layer features, a multi-layer context fusion module is developed to exchange the potentiality of multi-layer information to generate a high-resolution density map. Experimental results on four challenging datasets manifest that the newly proposed CAFNet can deliver impressive results compared with other state-of-the-art crowd counting models.

GTL-ASENet: global to local adaptive spatial encoder network for crowd counting

GTL-ASENet: global to local adaptive spatial encoder network for crowd counting

FPANet: feature pyramid attention network for crowd counting

FPANet: feature pyramid attention network for crowd counting