NDLI: Real-time pedestrian detection based on A hierarchical two-stage Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Kyoungwon Min Haengseon Son Yoonsik Choe Yong-Goo Kim
Copyright Year	2013
Description	Author affiliation: Dept. of Newmedia, KGIT, Seoul, South Korea (Yong-Goo Kim) \|\| Sch. of Electr. & Electron. Eng., Yonsei Univ., Seoul, South Korea (Yoonsik Choe) \|\| SoC Platform Center, Korea Electron. Technol. Inst., SeongNam, South Korea (Kyoungwon Min; Haengseon Son)
Abstract	This Paper presents an SVM (Support Vector Machine) based real-time pedestrian detection scheme for next-generation automotive vision applications. To meet the requirement of real-time detection with high accuracy, we designed the proposed system consisting of 2-stage hierarchical SVMs. In the proposed system, most of the input data are classified by the $1^{st}$ stage linear SVM and only the inputs between positive and negative hyper-plane of the linear SVM are transferred to the $2^{nd}$ stage non-linear SVM. This hierarchical 2-stage classifier can be suited for various systems via controlling the amount of data processed by the $2^{nd}$ stage classifier, which trades off the detection accuracy and the required system resources. To make the proposed $2^{nd}$ stage non-linear SVM further appropriate for various systems, a hyper-plane approximation technique by sample pruning has been adopted. By reducing the number of required SVs (Support Vectors) using this technique and controlling the amount of data processed via the $2^{nd}$ stage classifier, high precision non-linear SVM can be employed in the proposed real-time pedestrian detection system. Simulations using HOG (Histogram of Oriented Gradient) features and Daimler pedestrian dataset show the proposed system provides highly accurate classification results under the real-time constraint of application.
Starting Page	114
Ending Page	119
File Size	401931
Page Count	6
File Format	PDF
ISBN	9781467363204
e-ISBN	9781467363228
DOI	10.1109/ICIEA.2013.6566350
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-19
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Real-time systems Feature extraction Approximation methods Support vector machine classification Conferences Advanced Driver Assistant System Real-time Pedestrian Detection Support Vector Machine
Content Type	Text
Resource Type	Article

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