[1]
Tan Jiajia, Zhang Jianqiu, Novel vision-based method for real-time on road vehicle flow information extraction,. Chinese Journal of Scientific Instrument, vol. 29 , 2008, pp.158-166.
Google Scholar
[2]
Luo Xin, Zhu Qingxin, Improved traffic flow measuring system based on edge detection, Computer Engineering, vol. 32, 2006, pp.228-229.
Google Scholar
[3]
Zhang Ling, Chen Limin, He Wei, Guo Leimin, Application of an improved frame difference method based on video in traffic flow measurement, Transaction of Chong qing University, vol. 27, 2004, pp.31-33.
Google Scholar
[4]
Zhan Chunlin, Application of digital image processing in multilane vehicle detection, Guang zhou: Sun Yet University, (2005).
Google Scholar
[5]
Tan Xiaojun, Shen Wei, Guo Zhihao, Vision-based method for traffic flow surveillance on high way, Application Computer, vol. 25, 2005, pp.1215-1218.
Google Scholar
[6]
Ji Wenping, Guo Baolong, Ding Guiguang, Detecting traffic volume statistics based on virtual loop with optical flow method, Computer Simulation, vol. 21, 2004, pp.109-110.
Google Scholar
[7]
Luo Donghua, Yu Zhi, Li Xiying, Chen Ruixiang, Zhang Hui., Application of edge-based background difference in traffic volume extraction, Opto-Electronic Engineering, vol. 34, 2007, pp.70-73.
Google Scholar
[8]
Wu Jun, Xiao Zhitao, Improved virtual-line based video vehicle detection algorithm, Computer Engineering and Applications, vol. 44, 2008, pp.13-17.
Google Scholar
[9]
Guo Xin, Zhang Qi, Yang Liying, Dong Quan, Extraction of complex background with moving objects, Automation Grand Sight, vol. l0, 2006, pp.70-71.
Google Scholar
[10]
C. R. Wren, A. Azarbayjani, T. Darrell, A.P. Pentland, Pfinder: Real-Time tracking of the human body, Trans Pattern Analysis and Machine Intelligence, vol. 19, 1997, pp.780-785.
DOI: 10.1109/34.598236
Google Scholar
[11]
C. Stauffer, W. E. L. Grimson, Adaptive background mixture models for real-time tracking, Proceedings of Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO: IEEE, vol. 2, 1999, pp.246-252.
DOI: 10.1109/cvpr.1999.784637
Google Scholar
[12]
Lee Dar-Shyang, J. J. Hull, B. Erol, A bayesian framework for gaussian mixture background modeling, Proceedings of International Conference on Image Processing, Barcelona, Spain: IEEE, vol. 3, 2003, pp.973-976.
DOI: 10.1109/icip.2003.1247409
Google Scholar
[13]
Zoran Zivkovic, Improved adaptive gaussian mixture model for background subtraction, Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, United Kingdom: IEEE, vol. 2, 2004, pp.28-31.
DOI: 10.1109/icpr.2004.1333992
Google Scholar
[14]
Gao Dashan, Zhou Jie, Adaptive background estimation for real-time traffic monitoring, 2001 IEEE Intelligent Transportation Systems Conference Proceedings, Oakland (CA), USA: IEEE, 2001, pp.330-333.
DOI: 10.1109/itsc.2001.948678
Google Scholar
[15]
K. Toyama, J. Krumm, B. Brumitt, M. Brian, Wallflower: principles and practice of background maintenance, Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece: IEEE, vol. l, pp.255-261, (1999).
DOI: 10.1109/iccv.1999.791228
Google Scholar
[16]
Zhang Yongli, Zhang Taiyi, Bi Jianmin, A vehicle flow measuring algorithm with adaptive background extraction, Microelectronics & Computer, vol. 24, 2007, pp.138-140.
Google Scholar
[1]
[25] [28] [6] [3] 88.
Google Scholar
[25]
[0] [0] 100.
Google Scholar
[2]
[27] [32] [9] [4] 82.
Google Scholar
[26]
[0] [1] 96% TABLE II. The comparison of our algorithm with the edge background difference method Video Actual number Edge background difference Proposed algorithm Detected Number False-positive count False-negative count Detect rate Detected Number False-positive count False-negative count Detect rate.
DOI: 10.7717/peerj.7893/table-2
Google Scholar
[1]
[25] [27] [4] [2] 92.
Google Scholar
[25]
[0] [0] 100.
Google Scholar
[2]
[27] [30] [4] [1] 88.
Google Scholar
[26]
[0] [1] 96.
Google Scholar
