A monocular human detection system based on EOH and oriented LBP features

Authors:
Yingdong Ma;Xiankai Chen;Liu Jin;George Chen
Affiliations:
Center for Digital Media Computing, Shenzhen Institutes of Advanced Technology, Shenzhen, China;Center for Digital Media Computing, Shenzhen Institutes of Advanced Technology, Shenzhen, China;Center for Digital Media Computing, Shenzhen Institutes of Advanced Technology, Shenzhen, China;Center for Digital Media Computing, Shenzhen Institutes of Advanced Technology, Shenzhen, China
Venue:
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Year:
2011

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Abstract

This work introduces a fast pedestrian detection method that detects humans based on the boosted cascade approach using an on-board monocular camera. The Edge Orientation Histogram (EOH) feature and a novel Oriented Local Binary Patterns (Oriented LBP) feature are used in this system. Combination of these features captures salient features of humans and, together with a rejection cascade, achieves an efficient and accurate pedestrian detection system. Temporal coherence condition is employed to reject false positives from detection results. For a video sequence with resolution of 320 × 240 pixels, experiment results demonstrate that the proposed approach runs at about 16 frames/second, while maintaining an detection rate similar to existing methods.