Real-time pedestrian learning-tracking with information fusion

Authors:
Shan Gao;Zhenjun Han;Yang Xu;Qixiang Ye;Jianbin Jiao
Affiliations:
Graduate University of Chinese Academy of Sciences;Graduate University of Chinese Academy of Sciences;Tsinghua University;Graduate University of Chinese Academy of Sciences;Graduate University of Chinese Academy of Sciences
Venue:
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Year:
2012

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Abstract

This paper presents a novel and efficient object tracking method based on multi-sensor information fusion. Based on the popular detection-tracking framework, we consider the tracking process as 3 conditions and the fusion strategy can be adjusted in different conditions adaptively by designing a new online positive and negative sample classifier selecting method with the guidance of the depth information from the laser scanner. The results of our experiments show good robustness and performance when facing extreme cases such as the object rotation, long-period occlusion, and high similarity between object and background.