Multi-scale Detection Method for Dense Crowd Target Detection

Abstract

Abstract: A multi-scale algorithm was proposed to detect the targets flexibly. In coarse scale, an optimized DPM (Deformable Part Model) method was used to filter out sparse objectives that was represented by whole body. Then the whole scenario was cut into multiple finer regions and the Faster R-CNN (Region-based Convolutional Neural Network) method was trained and utilized to detect dense objects that was indicated by head in fine scale. These two detection results were incorporated via NMS (Non - Maximum Suppression) method, in order to supplement with each other and remove redundancy. The effectiveness of the proposed method has been proved comparing detect accuracy with DPM and R-CNN individually in the final experiment.

Key words: dense crowd target detection, multi-scale detection, DPM, Faster R-CNN

CLC Number:

TP18

Zhou Jianxin, Wu Jianjun, Xue Junqiang, Lin Shuai, Dang Gang, Cheng Zhiquan. Multi-scale Detection Method for Dense Crowd Target Detection[J]. Journal of System Simulation, 2016, 28(10): 2503-2509.

References 20

[1]	Dalal N, Triggs B.Histograms of oriented gradients for human detection[C]// Computer Vision and Pattern Recognition, 2005. CVPR 2005, IEEE Computer Society Conference on. USA: IEEE, 2005, 1: 886-893.
[2]	Felzenszwalb P F, Girshick R B, McAllester D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2010, 32(9): 1627-1645.
[3]	Felzenszwalb P F, Girshick R B, McAllester D. Cascade object detection with deformable part models[C]//Computer vision and pattern recognition (CVPR), 2010 IEEE conference on. USA:IEEE, 2010: 2241-2248.
[4]	Wang X, Yang M, Zhu S, et al.Regionlets for generic object detection[C]// Computer Vision (ICCV), 2013 IEEE International Conference on. USA: IEEE, 2013: 17-24.
[5]	Lempitsky V, Zisserman A.Learning to count objects in images[C]//Advances in Neural Information Processing Systems. Canada:NIPS,2010: 1324-1332.
[6]	Ren X, Ramanan D.Histograms of sparse codes for object detection[C]// Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on. USA: IEEE, 2013: 3246-3253.
[7]	Haroon Idrees, Imran Saleemi, Cody Seibert, Mubarak Shah.Muti-Source Multi-Scale Counting in Extremely Dense Crowd Images[C]// The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013. USA: IEEE, 2013: 2547-2554.
[8]	Bansal A, Venkatesh K S. People Counting in High Density Crowds from Still Images [J]. arXiv preprint arXiv (S2331-8422), 1507.08445, 2015. https://arxiv.org.
[9]	Krizhevsky A, Sutskever I, Hinton G E.Imagenet classification with deep convolutional neural networks[C]// Advances in Neural Information Processing Systems. Spain:NIPS , 2012: 1097-1105.
[10]	Girshick R, Donahue J, Darrell T, et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. USA:IEEE,2014: 580-587.
[11]	Girshick R.Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision. USA: IEEE, 2015: 1440-1448.
[12]	Ren S, He K, Girshick R, et al.Faster R-CNN: Towards real-time object detection with region proposal networks[C]//Advances in neural information processing systems.Canada:NIPS, 2015: 91-99.
[13]	Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection [J]. arXiv preprint arXiv (S2331-8422), 1506.02640,2015. https://arxiv.org.
[14]	Wang C, Zhang H, Yang L, et al.Deep People Counting in Extremely Dense Crowds[C]//Proceedings of the 23rd ACM international conference on Multimedia. Australia,ACM, 2015: 1299-1302.
[15]	Zeiler M D, Fergus R.Visualizing and Understanding Convolutional Networks [M]// Computer Vision - ECCV 2014. Springer International Publishing, 2013:818-833.
[16]	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [J]. arXiv preprint arXiv (S2331-8422), 1409.1556, 2014. https://arxiv.org.
[17]	Rodriguez M, Laptev I, Sivic J, et al.Density-aware person detection and tracking in crowds[C]//2011 International Conference on Computer Vision. Spain:IEEE, 2011: 2423-2430.
[18]	Felzenszwalb P F, Huttenlocher D P.Pictorial structures for object recognition[J]. International Journal of Computer Vision (S0920-5691), 2005, 61(1): 55-79.
[19]	Fischler M A, Elschlager R A.The representation and matching of pictorial structures[J]. IEEE Transactions on computers (S0018-9340), 1973, 22(1): 67-92.
[20]	Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition[C]// IEEE Transactions on Pattern Analysis and Machine Intelligence. USA: IEEE, 2015: 1904-1916.