|
|
|
| Zenithal Pedestrian Detection Based on Histogram of Oriented Gradient |
| TANG Chun-Hui |
Engineering Research Center of Optical Instrument and System of Ministry of Education,School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093
Shanghai Key Laboratory of Modern Optical System, School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology, Shanghai 200093 |
|
|
|
|
Abstract There are extensive researches on pedestrian detection, which mostly suppose visible humans observed in flat view and are applied in video surveillance,driving assistance etc. However, sometimes pedestrian detection from another perspective should be considered in practice. In this paper, a histogram of oriented gradients (HOG) descriptor is introduced for the zenithal pedestrian head detection. The vectors extracted from training samples are trained in the support vector machine to get the classifier parameters, and then the vectors of test samples are input into the classifier to discriminate which targets are. Compared with the existing methods, the proposed descriptor highlights both the region and the contour of the object. Partitioning blocks are reformed, and the feature calculation and statistical method are changed adaptively to the task. The experimental results show that the proposed method is effective and can be applied to count pedestrians in vertical view with a faster processing speed.
|
|
Received: 11 November 2013
|
|
|
|
|
|
[1] Enzweiler M, Gavrila D M. Monocular Pedestrian Detection: Survey and Experiments. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2179-2195
[2] University of Shanghai for Science and Technology. Long-Distance Passenger Overload Monitoring System: China, ZL2006100286220. 2006.7. 2008-12-31 (in Chinese)
(上海理工大学.长途客运超载监控系统:中国,ZL2006100286220. 2008-12-31)
[3] Terada K, Yoshida D, Oe S, et al. A Method of Counting the Passing People by Using the Stereo Images // Proc of the International Conference on Image Processing. Kobe, Japan, 1999, II: 338-342
[4] Yu H B, Fu W, Liu J L. Dynamic Contour Matching Approach in Vision-Based Passenger Flow Detection. Journal of Zhejiang University: Engineering Science, 2008. 42(3): 412-417 (in Chinese)
(于海滨,付 伟,刘济林.视觉客流检测中的动态轮廓匹配方法.浙江大学学报:工学版, 2008, 42(3): 412-417)
[5] Zhu Q Y, Tang L, Yu M, et al. A Passenger Counting Method Based on Stereovision. Journal of Image and Graphics, 2009. 14(11): 2391-2395 (in Chinese)
(朱秋煜,唐 利,郁 铭,等.一种基于立体视觉的公交车客流计数方法.中国图象图形学报, 2009. 14(11): 2391-2395)
[6] Barandiaran J, Murguia B, Boto F. Real-Time People Counting Using Multiple Lines // Proc of the 9th International Workshop on Image Analysis for Multimedia Interactive Services. Klagenfurt, Austria, 2008: 159-162
[7] Chen C H, Chang Y C, Chen T Y, et al. People Counting System for Getting in/out of a Bus Based on Video Processing // Proc of the 8th International Conference on Intelligent Systems Design and Applications. Kaohsiung, China, 2008, III: 565-569
[8] Anti＇c B, Leti＇c D, ＇Culibrk D, et al. K-means Based Segmentation for Real-Time Zenithal People Counting // Proc of the 16th IEEE International Conference on Image Processing. Cairo, Egypt, 2009: 2565-2568
[9] Chen T H, Chen T Y, Chen Z X. An Intelligent People-Flow Counting Method for Passing through a Gate // Proc of the IEEE Conference on Robotics, Automation and Mechatronics. Bangkok, Thailand, 2006: 1-6
[10] Yu H B, Liu J L. A Vision-Based Method to Estimate Passenger Flow in Bus. Journal of Image and Graphics, 2008, 13(4): 716-722 (in Chinese)
(于海滨,刘济林.应用于公交客流统计的机器视觉方法.中国图象图形学报, 2008, 13(4): 716-722)
[11] Tang C H, Chen Q J. Rapid Pedestrian Detection with Vertical-View Camera. Journal of System Simulation, 2012, 24(9): 1999-2002 (in Chinese)
(唐春晖,陈启军.一种快速的俯视行人检测方法.系统仿真学报, 2012, 24(9):1999-2002)
[12] Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005, I: 886-893
[13] Chang C C, Lin C J. LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Techno-logy, 2011.DOI:10.1145/1961189.1961199 |
|
|
|
2015, Vol. 28 
