Research on the Method of Quickly Finding the Pedestrian Area of Interest
Journal of Electrical and Electronic Engineering
Volume 5, Issue 5, October 2017, Pages: 180-185
Received: Nov. 16, 2017;
Published: Nov. 20, 2017
Views 265 Downloads 23
Yu Chun-he, School of Electronics and Information Engineering, Shenyang University of Aeronautics and Astronautics, Shenyang, China
Dong Cai-Fang, School of Electronics and Information Engineering, Shenyang University of Aeronautics and Astronautics, Shenyang, China
Follow on us
In pedestrian detection based on car video, it is necessary to quickly and accurately detect pedestrians. However, in the past the method that people used is exhaustive search. The method needs to detect all the areas in the picture, which is a waste of time. Based on the purpose of reducing the area to be tested, we use the edge detection and the principle of camera imaging, and run the test in MATLAB to get the method of finding the region of interest quickly. This method can reduce the retrieval area and shorten the retrieval time. The method can meet the requirements of real-time in pedestrian detection. Compared with the exhaustive search method, the number of windows that the method requires is 1/33 of the number of windows that the exhaustive search method requires. The detection speed of this method is several times higher than that of the exhaustive search method. It can be seen that the method is effective.
Edge Detection, Camera Imaging Principle, Vehicle Video Pedestrian Detection, Center Horizontal Line
To cite this article
Research on the Method of Quickly Finding the Pedestrian Area of Interest, Journal of Electrical and Electronic Engineering.
Vol. 5, No. 5,
2017, pp. 180-185.
Xi Yao. Pedestrian Detection and Tracking Based on On-board Vision Systems [D]. Beijing: Beijing Institute of Technology. 2015.
Jin Pei-fei, Zhou Li, Liu Jian, Ge Zhi-wei, Chen Jie. Pedestrian Detdction Based on Region of Interest Extracted by Support Vector Machine [J]. Computer Engineering and Desing, 2017, 38(4): 1 098-1 102.
Zhang Yin－hui, Liu Yang－shuo. Moving Object Detection Based on Method of Frame Difference and Background Subtraction [J]. Computer Technology and Development, 2017, 27(2): 25-28.
Li Liang, Luo Yi. Application Research of Inter-frame Difference in the Video Monitoring [J]. Journal of Sichuan University of Science & Engineering (Social Sciences Edition), 2015, 28 (6): 58-62.
Zhang Fan, Peng Zhong-wei, Meng Shui-jin. Improved Canny edge detection method based on self-adaptive threshold [J]. Journal of Computer Applications, 2012, 32( 8): 2296－2298.
Duan Jun, Gao Xiang. Adaptive Statistical Filtering Double Threshholds Based On Improved Canny Operator Edge Detection Algorithm [J]. Laser Journal, 2015, 36(1): 10-12.
Wang Xiao-kun, Jia Qing-xuan, Tan Sheng. Study on a monocular vision ranging method [J]. Science & Technology Information, 96-98.
Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection [C] //Proc. of IEEE Conference on Computer Vision and Pattern Recognition. San Diego, USA: [s. n.], 2005: 886-893.
Chen Yu-yuan, Guo Shu-qin, Lu Jun-jie, Zhang Biao. Pedestrian Detection in Video Monitoring Systems [J]. Journal of Hangzhou Dianzi University, 2014, 34 (1): 95-98.
Zhnag Yang, Liu Wei-ming, Wu Yi-hu. A Fast Pedestrian Detection Method for Vehicle Auxiliary Driving System [J]. Journal of Highway and Transportation Research and Development, 2013, 30(11): 131-138.