基于深度时空特征卷积—池化的视频人群计数方法

doi:10.11959/j.issn.1000-0801.2018161

Abstract

Abstract:

Due to angle of camera,background,population density distribution and occlusion limitations,traditional video crowd counting methods based on underlying visual features are often difficult to achieve ideal results.Using the temporal and spatial features of video and conv-pooling method,high-level visual features were formed,local feature aggregation descriptors were used for quantization and codebook calculation to achieve accurate description of video crowd information.This method made full use of video motion and appearance information.Based on convolutional neural networks and pooling methods,the ability to describe video intrinsic attributes and features was improved.Experimental results show that the proposed method has higher precision and better robustness than traditional video crowd counting methods.

Key words: crowd counting, convolutional neural network, deep spatial and temporal feature, conv-pooling

CLC Number:

TP391

Qiang LI,Zilu KANG. Video crowd counting method based on conv-pooling deep spatial and temporal features[J]. Telecommunications Science, 2018, 34(6): 72-79.

Figures/Tables 10

References 22

[5]	SIMONYAN K , ZISSERMAN A . Very deep convolutional networks for large-scale image recognition[J]. Computer Science, 2014(9).
[6]	TRAN D , BOURDEV L , FERGUS R ,et al. Learning spatiotemporal features with 3D convolutional networks[C]// The 2015 IEEE International Conference on Computer Vision (ICCV’15),Dec 7-13,2015,Santiago,Chile. Piscataway:IEEE Press, 2015: 4489-4497.
[7]	J’EGOU H , DOUZE M , SCHMID C ,et al. Aggregating local descriptors into a compact image representation[C]// The 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’10),June 13-18,2010,San Francisco,CA,USA. Piscataway:IEEE Press, 2010: 3304-3311.
[8]	VIOLA P , JONES M J . Robust real-time face detection[J]. International Journal of Computer Vision, 2004,57(2): 137-154.
[9]	WU B , NEVATIA R . Detection of multiple,partially occluded humans in a single image by bayesian combination of edgelet part detectors[C]// The 2005 IEEE International Conference on Computer Vision (ICCV’05),Oct 17-21,2005,Beijing,China. Piscataway:IEEE Press, 2005: 90-97.
[10]	SABZMEYDANI P , MORI G . Detecting pedestrians by learning shapelet features[C]// The 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’07),June 18-23,2007,Minneapolis,Minnesota,USA. Piscataway:IEEE Press, 2007: 1-8.
[11]	GALL J , YAO A , RAZAVI N ,et al. Hough forests for object detection,tracking,and action recognition[J]. IEEE Transactions on Pattern Analysis ＆ Machine Intelligence, 2011,33(11): 2188-2202.
[12]	HUANG D , SHAN C , ARDABILIAN M ,et al. Local binary patterns and its application to facial image analysis:a survey[J]. IEEE Transactions on Systems Man ＆ Cybernetics Part C Applications ＆ Reviews, 2011,41(6): 765-781.
[13]	SULOCHANA S , VIDHYA R . Texture based image retrieval using framelet transform–gray level co-occurrence matrix(GLCM)[J]. International Journal of Advanced Research in Artificial Intelligence, 2013,2(2).
[14]	HINTON G E , SALAKHUTDINOV R . Reducing the dimensionality of data with neural networks[J]. Science, 2006,313(5786): 504-507.
[15]	ZHANG C , LI H , WANG X ,et al. Cross-scene crowd counting via deep convolutional neural networks[C]// The 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15),June 8-10,2015,Boston,Massachusetts,USA. Piscataway:IEEE Press, 2015: 833-841.
[16]	CHAN A , LIANG Z , Vasconcelos N . Privacy preserving crowd monitoring:counting people without people models or tracking[C]// The 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’08),June 24-26,2008,Anchorage,Alaska,USA. Piscataway:IEEE Press, 2008: 1-7.
[17]	时增林, 叶阳东, 吴云鹏 ,等. 基于序的空间金字塔池化网络的人群计数方法[J]. 自动化学报, 2016,42(6): 866-874.
	SHI Z L , YE Y D , WU Y P ,et al. Crowd counting using rank-based spatial pyramid pooling network[J]. Acta Automatica Sinica, 2016,42(6): 866-874.
[18]	BOOMINATHAN L , KRUTHIVENTI S , BABU R . Crowdnet:a deep convolutional network for dense crowd counting[C]// The 2016 ACM Conference on Multimedia Conference (MM’16),Oct 15-19,2016,Amsterdam,The Netherlands. New York:ACM Press, 2016: 640-644.
[19]	ZACH C , POCK T , BISCHOF H . A duality based approach for realtime TV-L1 optical flow[M]. Berlin: SpringerPress, 2007: 214-223.
[20]	BAY H , ESS A , TUYTELAARS T ,et al. Speeded-up robust features (SURF)[J]. Computer Vision and Image Understanding, 2008,110(3): 346-359
[21]	FISCHLER M A , BOLLES R C . Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981,24(6): 381-395.
[1]	LOY C C , CHEN K , GONG S G ,et al. Crowd counting and profiling:methodology and evaluation[M]. New York: SpringerPress, 2013: 347-382.
[2]	IDREES H , SALEEMI I , SEIBERT C ,et al. Multi-source multi-scale counting in extremely dense crowd images[C]// The 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’13),June 23-28,2013,Portland,OR,USA. Piscataway:IEEE Press, 2013: 2547-2554.
[3]	ROSTEN E , PORTER R , DRUMMOND T . Faster and better:a machine learning approach to corner detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010,32(1): 105-119.
[4]	DALAL N , TRIGGS B . Histograms of oriented gradients for human detection[C]// The 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’05),June 20-25,2005,New York,NY,USA. Piscataway:IEEE Press, 2005: 886-893.
[22]	ZHANG Z X , WANG M , GENG X . Crowd counting in public video surveillance by label distribution learning[J]. Neurocomputing, 2015(166): 151-163.

Metrics

Recommended 0

No Suggested Reading articles found!

数据集	N_f	R	FPS	D	T_p
UCSD	2 000	238×158像素	10	11~46	49 885
Mall	2 000	640×480像素	<2	13~53	62 325

数据集	K=32	K=64	K=128	K=256	K=512
UCSD	47%	50%	52%	58%	55%
Mall	51%	57%	61%	66%	62%

方法	UCSD		Mall
方法	MAE	MSE	MAE	MSE
参考文献[16]方法	2.24	7.97	2.88	13.15
参考文献[17]方法	1.89	5.43	2.54	10.09
参考文献[22]方法	2.08	7.25	2.69	12.10
本文方法	1.81	5.25	2.38	9.29

特征类型	UCSD		Mall
特征类型	MAE	MSE	MAE	MSE
时域特征	2.67	8.52	3.37	14.70
空域特征	2.31	8.13	2.99	13.38
时空特征	1.81	5.25	2.38	9.29

Video crowd counting method based on conv-pooling deep spatial and temporal features

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 22

Related Articles 5

Metrics

Recommended 0

[1]	Liang CHEN, Yaguan QIAN, Zhiqiang HE, Xiaohui GUAN, Bin WANG, Xing WANG. A flexible pruning on deep convolutional neural networks [J]. Telecommunications Science, 2022, 38(1): 83-94.
[2]	Zihao LIU, Xiaojun JIA, Sulan ZHANG, Zhiling XU, Jun ZHANG. Vibe++ background segmentation method combining MeanShift clustering analysis and convolutional neural network [J]. Telecommunications Science, 2021, 37(3): 133-145.
[3]	Jingcheng CAO, Jidong ZHANG, Guojie SHI. A method for improving recall rate of similar image retrieval by using edge enhancement technology [J]. Telecommunications Science, 2021, 37(1): 76-84.
[4]	Hongwei XU,Diqun YAN,Fan YANG,Rangding WANG,Chao JIN,Li XIANG. Detection algorithm of electronic disguised voice based on convolutional neural network [J]. Telecommunications Science, 2018, 34(2): 46-57.
[5]	Huijun XING,Shuo CHANG. Pedestrian surveillance system based on mobile vehicle [J]. Telecommunications Science, 2017, 33(2): 120-127.