Browse > Article
http://dx.doi.org/10.5909/JBE.2016.21.4.493

Pedestrian Counting System based on Average Filter Tracking for Measuring Advertisement Effectiveness of Digital Signage  

Kim, Kiyong (Department of Computer Science and Engineering, Konkuk University)
Yoon, Kyoungro (Department of Computer Science and Engineering, Konkuk University)
Publication Information
Journal of Broadcast Engineering / v.21, no.4, 2016 , pp. 493-505 More about this Journal
Abstract
Among modern computer vision and video surveillance systems, the pedestrian counting system is a one of important systems in terms of security, scheduling and advertising. In the field of, pedestrian counting remains a variety of challenges such as changes in illumination, partial occlusion, overlap and people detection. During pedestrian counting process, the biggest problem is occlusion effect in crowded environment. Occlusion and overlap must be resolved for accurate people counting. In this paper, we propose a novel pedestrian counting system which improves existing pedestrian tracking method. Unlike existing pedestrian tracking method, proposed method shows that average filter tracking method can improve tracking performance. Also proposed method improves tracking performance through frame compensation and outlier removal. At the same time, we keep various information of tracking objects. The proposed method improves counting accuracy and reduces error rate about S6 dataset and S7 dataset. Also our system provides real time detection at the rate of 80 fps.
Keywords
pedestrian detection; digital signage; people counting; pedestrian tracking; average filter;
Citations & Related Records
연도 인용수 순위
  • Reference
1 R. Benenson, M. Mathias, R. Timofte and L. Van Gool, “Pedestrian detection at 100 frames per second”, 『In Computer Vision and Pattern Recognition (CVPR)』, 2012 IEEE Conference on, pages 2903-2910. IEEE, 2012.
2 P. Dollar, S. Belongie, and P. Perona, “The fastest pedestrian detector in the west”, 『British Machine Vision Conference (BMVC)』, 2010.
3 R. Benenson, R. Timofte, and L. Van Gool, “Stixels estimation without depthmap computation”, 『In ICCV, CVVT workshop』, 2011.
4 C. Zeng, H. Ma, “Robust head-shoulder detection by PCA-based multilevel HOG-LBP detector for people counting”, 『In Proc. International Conference on Pattern Recognition』, 2010.
5 F. Chen and E. Zhang, “A fast and robust people counting method in video surveillance”, 『In Proc. IEEE International Conference on Computational Intelligence and Security』, 2007.
6 H. Fu, H. Ma, and H. Xiao, “Real-time accurate crowd counting based on rgb-d information, 『In Proc. IEEE International Conference on Information Processing (ICIP)』, 2012.
7 O. Sidla, Y. Lypetskyy, N. Brandle, and S. Seer, “Pedestrian detection and tracking for counting applications in crowded situations”, 『In Proc. IEEE International Conference on Video and Signal Based Surveillance』, 2006.
8 D. Ryan, S. Denman, C. Fookes, and S. Sridharan, “Crowd counting using group tracking and local features”, 『In Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)』, 2010.
9 V.B. Subburaman, A. Descamps, C. Carincotte, “Counting people in the crowd using a generic head detector”, 『In 2012 IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS)』, 2012.
10 D. Kong, D. Gray, and H. Tao, “A viewpoint invariant approach for crowd counting”, 『In Proc. International Conference on Pattern Recognition』, 2006.
11 D. Ryan, S. Denman, C. Fookes, and S. Sridharan, “Crowd counting using multiple local features”, 『In Digital Image Computing: Techniques and Applications (DICTA)』, 2009.
12 H. Fradi, and J.-L. Dugelay, “Low level crowd analysis using frame-wise normalized feature for people counting”, 『In IEEE International Workshop on Information Forensics and Security』, 2012.
13 H. Fradi, and J.-L. Dugelay, “People counting system in crowded scenes based on feature regression”, 『In EUSIPCO 2012, European Signal Processing Conference』, 2012.
14 J. Li, L. Huang, and C. Liu, “Robust people counting in video surveillance: dataset and system”, 『In 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS)』, 2011.
15 S. Fujisawa, G. Hasegawa, Y. Taniguchi, and H. Nakano, "Pedestrian counting in video sequences based on optical flow clustering" 『International Journal of Image Processing』, 2013.