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Real-time Superpixels Based Tracking Method |
WANG Wei WANG Chunping FU Qiang XU Yan |
(The 2nd Department, Ordnance Engineering College, Shijiazhuang 050003, China) |
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Abstract Target appearance model is crucial for tracking. In this paper, a Real-time SuperPixels based Tracking (RSPT) method is proposed in a tracking-by-detection framework, by investigating mid-level vision cue superpixels. Firstly, a discriminative appearance model is constructed relying superpixels feature and K-Nearest Neighbor (KNN) learning method. Then the tracking problem is posed by computing a confidence map, and detecting the best target station by maximizing an object location likelihood function. The integral image data structure is adopted for fast detection, innovatively. Implemented in MATLAB without code optimization, the proposed tracker runs at 19 frames per second on an i5 laptop. Extensive experimental results on challenging sequences show that the proposed algorithm performs favorably against some state-of-the-art methods in terms of accuracy and robustness.
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Received: 08 June 2015
Published: 03 February 2016
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Fund: The National Natural Science Foundation of China (61141009) |
Corresponding Authors:
WANG Wei
E-mail: wang_wei.buaa@163.com
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