Robust pedestrian detection in thermal infrared imagery using a shape distribution histogram feature and modified sparse representation classification. Issue 6 (June 2015) - Zhao, Xinyue; He, Zaixing; Zhang, Shuyou; Liang, Dong
Abstract In this paper, a robust approach using a shape distribution histogram (SDH) feature and modified sparse representation classification (MSRC) for pedestrian detection in thermal infrared imagery is proposed. In this framework, the candidate regions that are more likely to contain the pedestrians are first detected based on the Contour Saliency Map. Then distances between random points on the thinned contour map of objects in the candidate regions are applied to acquire the SDH feature. SDH is a robust and discriminative feature which can precisely describe the pedestrian characteristics. Finally, a robust MSRC classifier which has high accuracy is used to recognize the true pedestrians. Experiments are conducted over the OSU thermal pedestrian database by comparing with other algorithms. The proposed method shows an excellent performance in detecting pedestrians.