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논문 기본 정보

자료유형
학술저널
저자정보
Thanh Binh Nguyen (Soongsil University) Van Tuan Nguyen (Soongsil University) Sun-Tae Chung (Soongsil University)
저널정보
한국멀티미디어학회 멀티미디어학회논문지 멀티미디어학회논문지 제18권 제11호
발행연도
2015.11
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1,289 - 1,301 (13page)

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초록· 키워드

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Pedestrian detection (PD) is an essential task in various applications and sliding window-based methods utilizing HOG (Histogram of Oriented Gradients) or HOG-like descriptors have been shown to be very effective for accurate PD. However, due to exhaustive search across images, PD methods based on sliding window usually require heavy computational time. In this paper, we propose a real-time PD method for embedded visual surveillance with fixed backgrounds. The proposed PD method employs HOG descriptors as many PD methods does, but utilizes selective search so that it can save processing time significantly. The proposed selective search is guided by restricting searching to candidate regions extracted from Adaptive Gaussian Mixture Model (AGMM)-based background subtraction technique. Moreover, approximate computation of HOG descriptor and implementation in fixed-point arithmetic mode contributes to reduction of processing time further. Possible accuracy degradation due to approximate computation is compensated by applying an appropriate one among three offline trained SVM classifiers according to sizes of candidate regions. The experimental results show that the proposed PD method significantly improves processing speed without noticeable accuracy degradation compared to the original HOG-based PD and HOG with cascade SVM so that it is a suitable real-time PD implementation for embedded surveillance systems.

목차

ABSTRACT
1. INTRODUCTION
2. BACKGROUNDS AND RELATED WORKS
3. THE PROPOSED REAL-TIME PEDESTRIAN DETECTION
4. EXPERIMENTAL RESULTS
5. CONCLUSIONS
REFERENCE

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UCI(KEPA) : I410-ECN-0101-2016-004-002159941