Improving Crowd Counting with Multi-Task Multi-Scale Convolutional Neural Network

Counting the number of person has received much attention in recent years. Most of the existing crowd counting methods adopted density map regression pipeline, which formulates the crowd counting problem to two fragmented part: density map regression and integration of the overall counting. To solve this problem, this paper presents a multi-task deep learning scheme to enhance the counting performance. More specifically, we firstly build a multi-scale deep convolutional neural network, based on combining the feature maps of conv layers with different filters, to solve the multi-scale problem in crowd counting. Secondly, we develop the multi-task structure that can simultaneously learn the density map and the global counting. Experiments on large scale crowd counting datasets, Shanghaitech and WorldExpo10, demonstrate that the proposed method achieves much reduction in counting error respectively

[1]  Zhengyou Zhang,et al.  Improving multiview face detection with multi-task deep convolutional neural networks , 2014, IEEE Winter Conference on Applications of Computer Vision.

[2]  Zhaoxiang Zhang,et al.  Crowd density estimation based on statistical analysis of local intra-crowd motions for public area surveillance , 2012 .

[3]  Yan Wang,et al.  Dense crowd counting from still images with convolutional neural networks , 2016, J. Vis. Commun. Image Represent..

[4]  Andrew Zisserman,et al.  Learning To Count Objects in Images , 2010, NIPS.

[5]  Shihong Lao,et al.  Robust crowd counting using detection flow , 2011, 2011 18th IEEE International Conference on Image Processing.

[6]  Pietro Perona,et al.  Integral Channel Features , 2009, BMVC.

[7]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[8]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Xiaogang Wang,et al.  Cross-scene crowd counting via deep convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Grantham Pang,et al.  People Counting and Human Detection in a Challenging Situation , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[11]  Xiaoou Tang,et al.  Facial Landmark Detection by Deep Multi-task Learning , 2014, ECCV.

[12]  Nuno Vasconcelos,et al.  Privacy preserving crowd monitoring: Counting people without people models or tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Haizhou Ai,et al.  End-to-end crowd counting via joint learning local and global count , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[14]  Xiaochun Cao,et al.  Deep People Counting in Extremely Dense Crowds , 2015, ACM Multimedia.

[15]  Haidi Ibrahim,et al.  Recent survey on crowd density estimation and counting for visual surveillance , 2015, Eng. Appl. Artif. Intell..

[16]  Daniel Oñoro-Rubio,et al.  Towards Perspective-Free Object Counting with Deep Learning , 2016, ECCV.

[17]  Shenghua Gao,et al.  Single-Image Crowd Counting via Multi-Column Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).