Crowd counting with segmentation attention convolutional neural network

  title={Crowd counting with segmentation attention convolutional neural network},
  author={Jiwei Chen and Zengfu Wang},
  journal={IET Image Process.},
Deep learning occupies an undisputed dominance in crowd counting. This paper proposes a novel convolutional neural network architecture called SegCrowdNet. Despite the complex background in crowd scenes, the proposed SegCrowdNet still adaptively highlights the human head region and suppresses the non-head region by segmentation. With the guidance of an attention mechanism, the proposed SegCrowdNet pays more attention to the human head region and automatically encodes the highly refined density… 

Figures and Tables from this paper

Crowd density estimation based on multi scale features fusion network with reverse attention mechanism

A multi-scale feature fusion network (IA-MFFCN) based on the reverse attention mechanism, which maps the image to the crowd density map for counting, and a comprehensive loss function based on Euclidean loss and predicted population loss is designed to improve training accuracy, to produce a more accurate density value.

SSR-HEF: Crowd Counting with Multi-Scale Semantic Refining and Hard Example Focusing

This work is the first to propose the hard example focusing (HEF) algorithm for the regression task of crowd counting and proposes a multiscale semantic refining strategy to break through the limitation of deep learning to capture semantic features of different scales to sufficiently deal with the scale variation.



Cross-scene crowd counting via deep convolutional neural networks

A deep convolutional neural network is proposed for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count, to obtain better local optimum for both objectives.

Switching Convolutional Neural Network for Crowd Counting

A novel crowd counting model that maps a given crowd scene to its density and switch convolutional neural network that leverages variation of crowd density within an image to improve the accuracy and localization of the predicted crowd count is proposed.

Crowd Counting with Segmentation Map Guidance

A new deep model to integrate a segmentation map to compensate for the false response under a complex environment is proposed and a new method to generate segmentation ground truth merely based on the density map instead of manual labeling is proposed.

Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

With the proposed simple MCNN model, the method outperforms all existing methods and experiments show that the model, once trained on one dataset, can be readily transferred to a new dataset.

CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting

A novel end-to-end cascaded network of CNNs to jointly learn crowd count classification and density map estimation achieves lower count error and better quality density maps as compared to the recent state-of-the-art methods.

CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

This work uses a combination of deep and shallow, fully convolutional networks to predict the density map for a given crowd image, and shows that this combination is used for effectively capturing both the high-level semantic information and the low-level features, necessary for crowd counting under large scale variations.

Scene invariant crowd counting using multi-scales head detection in video surveillance

A novel crowd counting method based on multi-scales head detection that overcomes the traditional detecting method's deficiencies of low accuracy when facing perspective transformation.