NDLI: Pedestrian detection using background subtraction assisted Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Yuan Xu Lihong Xu Dawei Li Yang Wu
Copyright Year	2011
Description	Author affiliation: Dept.of Control Science and Engineering, Tongji University, Shanghai, China (Yuan Xu; Lihong Xu; Dawei Li; Yang Wu)
Abstract	This paper achieves fast and effective pedestrian detection using Histogram of Oriented Gradient (HOG) descriptor based Support Vector Machine (SVM). A novel approach taking advantage of CodeBook background subtraction(CBBS) is presented in this paper to produce pedestrian samples for SVM. HOG features of the samples are extracted to train Linear and RBF SVM classifiers offline. The classifier is adopted as pedestrian detector in online real-time video sequence detection. The influence of various ratios of positive and negative training sets on detector's performance is carefully investigated. We also compare Linear and RBF SVM in experiments as well. It is concluded that robust feature extraction, proper positive and negative training sample construction, and fine kernel function are crucial for good classification results. Experiments prove that our detector obtains a reliable detection result, which not only satisfies real-time requirement, and is robust against pedestrian appearance and pose variations, illumination changes, background changes, shadows and etc.
Starting Page	837
Ending Page	842
File Size	713706
Page Count	6
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121761
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training HOG descriptor Histograms Machine learning Streaming media Feature extraction Codebook background subtraction Pedestrian detection SVM Kernel Testing
Content Type	Text
Resource Type	Article

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