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Traffic extreme situations detection in video sequences based on integral optical flow

H. Chen1, S. Ye1, A. Nedzvedz 3,4, O. Nedzvedz 2, H. Lv1, S. Ablameyko 3,4

1 Zhejiang Shuren University, Hangzhou, China,

2 Belarusian State Medical University, Minsk, Belarus,

3 Belarusian State University, Minsk, Belarus,

4 United Institute of Informatics Problems of National Academy of Sciences, Minsk, Belarus

 PDF, 1049 kB

DOI: 10.18287/2412-6179-2019-43-4-647-652

Pages: 647-652.

Full text of article: English language.

Abstract:
Road traffic analysis is an important task in many applications and it can be used in video surveillance systems to prevent many undesirable events. In this paper, we propose a new method based on integral optical flow to analyze cars movement in video and detect flow extreme situations in real-world videos. Firstly, integral optical flow is calculated for video sequences based on optical flow, thus random background motion is eliminated; secondly, pixel-level motion maps which describe cars movement from different perspectives are created based on integral optical flow; thirdly, region-level indicators are defined and calculated; finally, threshold segmentation is used to identify different cars movements. We also define and calculate several parameters of moving car flow including direction, speed, density, and intensity without detecting and counting cars. Experimental results show that our method can identify cars directional movement, cars divergence and cars accumulation effectively.

Keywords:
integral optical flow, image processing, road traffic control, video surveillance

Citation:
Chen H, Ye S, Nedzvedz A, Nedzvedz O, Lv H, Ablameyko S. Traffic extreme situations detection in video sequences based on integral optical flow. Computer Optics 2019; 43(4): 647-652. DOI: 10.18287/2412-6179-2019-43-4-647-652.

References:
  1. Al-Sakran HO. Intelligent traffic information system based on integration of internet of things and agent technology, International Journal of Advanced Computer Science and Applications 2015; 6: 37-43.
  2. Ao GC, Chen HW, Zhang HL. Discrete analysis on the real traffic flow of urban expressways and traffic flow classification. Advances in Transportation Studies 2017; 1(Spec Iss): 23-30.
  3. Cao J. Research on urban intelligent traffic monitoring system based on video image processing. International Journal of Signal Processing, Image Processing and Pattern Recognition 2016; 9: 393-406.
  4. Rodríguez T, García N. An adaptive, real-time, traffic monitoring system. Mach Vis Appl 2010; 21: 555-576.
  5. Kastrinaki V, Zervakis M, Kalaitzakis K. A survey of video processing techniques for traffic applications. Image and Vision Computing 2003; 21: 359-381.
  6. Huang DY, Chen CH, Hu WC, et al. Reliable moving vehicle detection based on the filtering of swinging tree leaes and raindrops. J Vis Comun Image Represent 2012; 23: 648-664.
  7. Zhang W, Wu QMJ, Yin HB. Moving vehicles detection based on adaptive motion histogram. Digit Signal Process 2010; 20: 793-805.
  8. Joshi A, Mishra D. Review of traffic density analysis techniques. International Journal of Advanced Research in Computer and Communication Engineering 2015; 4(7): 209-213.
  9. Nagaraj U, Rathod J, Patil P, Thakur S, Sharma U. Traffic jam detection using image processing. International Journal of Engineering Research and Applications 2013; 3(2): 1087-1091.
  10. Shafie AA, Ali MH, Fadhlan H, Ali RM. Smart video surveillance system for vehicle detection and traffic flow control. Journal of Engineering Science and Technology 2011; 6(4): 469-480.
  11. Khanke P, Kulkarni PS. A technique on road traffic analysis using image processing. International Journal of Engineering Research and Technology 2014; 3: 2769-2772.
  12. Kamath VS, Darbari M, Shettar R. Content based indexing and retrieval from vehicle surveillance videos using optical flow method. Int J Sci Research 2013; II(IV): 4-6.
  13. Cheng J, Tsai YH, Wang S, Yang MH. SegFlow: Joint learning for video object segmentation and optical flow. Proceedings of International Conference on Computer Vision 2017: 686-695.
  14. Zhang W, Hou Y, Wang S. Event recognition of crowd video using corner optical flow and convolutional neural network. Proceeding of Eighth International Conference on Digital Image Processing 2016: 332-335.
  15. Ravanbakhsh M, Nabi M, Mousavi H, Sangineto E, Sebe N. Plug-and-play CNN for crowd motion analysis: An application in abnormal event detection. Source: <https://arxiv.org/abs/1610.00307>.
  16. Andrade EL, Blunsden S, Fisher RB. Modelling crowd scenes for event detection. Proceedings of 18th International Conference on Pattern Recognition 2006:1: 175-178.
  17. Wang Q, Ma Q, Luo CH, Liu HY, Zhang CL. Hybrid histogram of oriented optical flow for abnormal behavior detection in crowd scenes. International Journal of Pattern Recognition and Artificial Intelligence 2016; 30(2): 210-224.
  18. Mehran R, Oyama A, Shah M. Abnormal crowd behavior detection using social force model. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 2009: 935-942.
  19. Chen C, Ye S, Chen H, Nedzvedz O, Ablameyko S. Integral optical flow and its applications for dynamic object monitoring in video. J Appl Spectrosc 2017; 84: 120-128.
  20. Chen H, Ye S, Nedzvedz O, Ablameyko S. Application of integral optical flow for determining crowd movement from video images obtained using video surveillance systems. J Appl Spectrosc 2018; 85: 126-133.

     

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