基于 BING-casDPM 的快速行人检测算法

doi:10.12066/j.issn.1007-2861.1988

Abstract

Abstract:

Pedestrian detection is a hot research field in computer vision technology, and it has important applications in automobile assisted driving and video surveillance. In view of the fact that traditional deformable part model (DPM) uses a sliding window for pedestrian detection and is time-consuming in the background area, a fast pedestrian detection algorithm based on BING-casDPM is proposed. Firstly, a cascaded support vector machine (SVM) classifier is trained based on the binarized normed gradients (BING) feature. The candidate areas containing all kinds of objects as seen on the test image are quickly calibrated through the classifier. Then, the detected box is extracted according to the characteristics of candidate windows, and finally pedestrians are accurately detected from the detected box using casDPM model with the result being returned to the original image. The experiment results show that the algorithm proposed in this paper is about 16 times faster than the classical DPM model, and about 40% faster than the casDPM model in terms of the speed of detection when detection rate remains stable.

Key words: pedestrian detection, binarized normed gradient(BING), detected box, star-cascade deformable part model (casDPM)

CLC Number:

TP312

Yanwei HU, Meihua XU, Aiying GUO. Fast pedestrian detection algorithm based on BING-casDPM[J]. Journal of Shanghai University（Natural Science Edition）, 2019, 25(5): 712-721.

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References 19

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Fast pedestrian detection algorithm based on BING-casDPM

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