Differential trajectory tracking with automatic learning of background reconstruction
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Nowadays, trajectory tracking technology is widely used in many outdoor applications, such as intelligent traffic and video surveillance. However, most of trajectory-tracking technologies rely on a static background, which is hard to obtain in many situations. Obviously, these methods are out of action in the case of dynamic background. In this paper, a novel trajectory tracking method is presented, which is implemented with a new background reconstruction algorithm. Firstly, the background is assumed to be a blank scene. Then, the background is reconstructed by means of video detection that places moving objects in the scene. Finally, real-time trajectories of moving objects are computed based on the reconstructed background. Experimental results show its robustness and practicability even in a cluttered background.
KeywordsTrajectory tracking Background reconstruction Direction Automatic learning Real-time
This work is supported by National Natural Science Foundation of China [No: 61262082,61261019, 61461039], Key Project of Chinese Ministry of Education [No.212025], Scientific Projects of Higher School of Inner Mongolia [No. NJZY13004], Natural Science Foundation of Inner Mongolia [No.2014BS0606], Inner Mongolia Science Foundation for Distinguished Young Scholars [2012JQ03], Enhancing Comprehensive Strength Foundation of Inner Mongolia University [No. 14020202],Program of Higher-level talents of Inner Mongolia University (125130, 135103).
The authors would like to express their heartfelt gratitude to all the volunteers in the experiments and the anonymous reviewers, for their help on this paper.
Conflict of interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
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