28 April 2017 Adaptive learning compressive tracking based on Markov location prediction
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Object tracking is an interdisciplinary research topic in image processing, pattern recognition, and computer vision which has theoretical and practical application value in video surveillance, virtual reality, and automatic navigation. Compressive tracking (CT) has many advantages, such as efficiency and accuracy. However, when there are object occlusion, abrupt motion and blur, similar objects, and scale changing, the CT has the problem of tracking drift. We propose the Markov object location prediction to get the initial position of the object. Then CT is used to locate the object accurately, and the classifier parameter adaptive updating strategy is given based on the confidence map. At the same time according to the object location, extract the scale features, which is able to deal with object scale variations effectively. Experimental results show that the proposed algorithm has better tracking accuracy and robustness than current advanced algorithms and achieves real-time performance.
© 2017 SPIE and IS&T
Xingyu Zhou, Xingyu Zhou, Dongmei Fu, Dongmei Fu, Tao Yang, Tao Yang, Yanan Shi, Yanan Shi, } "Adaptive learning compressive tracking based on Markov location prediction," Journal of Electronic Imaging 26(2), 023026 (28 April 2017). https://doi.org/10.1117/1.JEI.26.2.023026 . Submission: Received: 7 December 2016; Accepted: 6 April 2017
Received: 7 December 2016; Accepted: 6 April 2017; Published: 28 April 2017

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