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Proceedings Paper

Pedestrian detection based on redundant wavelet transform
Author(s): Lin Huang; Liping Ji; Ping Hu; Tiejun Yang
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Paper Abstract

Intelligent video surveillance is to analysis video or image sequences captured by a fixed or mobile surveillance camera, including moving object detection, segmentation and recognition. By using it, we can be notified immediately in an abnormal situation. Pedestrian detection plays an important role in an intelligent video surveillance system, and it is also a key technology in the field of intelligent vehicle. So pedestrian detection has very vital significance in traffic management optimization, security early warn and abnormal behavior detection. Generally, pedestrian detection can be summarized as: first to estimate moving areas; then to extract features of region of interest; finally to classify using a classifier. Redundant wavelet transform (RWT) overcomes the deficiency of shift variant of discrete wavelet transform, and it has better performance in motion estimation when compared to discrete wavelet transform. Addressing the problem of the detection of multi-pedestrian with different speed, we present an algorithm of pedestrian detection based on motion estimation using RWT, combining histogram of oriented gradients (HOG) and support vector machine (SVM). Firstly, three intensities of movement (IoM) are estimated using RWT and the corresponding areas are segmented. According to the different IoM, a region proposal (RP) is generated. Then, the features of a RP is extracted using HOG. Finally, the features are fed into a SVM trained by pedestrian databases and the final detection results are gained. Experiments show that the proposed algorithm can detect pedestrians accurately and efficiently.

Paper Details

Date Published: 31 October 2016
PDF: 7 pages
Proc. SPIE 10020, Optoelectronic Imaging and Multimedia Technology IV, 1002017 (31 October 2016); doi: 10.1117/12.2246044
Show Author Affiliations
Lin Huang, Guilin Univ. of Technology (China)
Liping Ji, Guilin Univ. of Technology (China)
Ping Hu, Guilin Univ. of Technology (China)
Tiejun Yang, Guilin Univ. of Technology (China)

Published in SPIE Proceedings Vol. 10020:
Optoelectronic Imaging and Multimedia Technology IV
Qionghai Dai; Tsutomu Shimura, Editor(s)

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