Real-time Superpixels Based Tracking Method

WANG Wei; WANG Chunping; FU Qiang; XU Yan

doi:10.11999/JEIT150705

Volume 38 Issue 3

Mar. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(3): 571-577

WANG Wei, WANG Chunping, FU Qiang, XU Yan. Real-time Superpixels Based Tracking Method[J]. Journal of Electronics & Information Technology, 2016, 38(3): 571-577. doi: 10.11999/JEIT150705

Citation:

WANG Wei, WANG Chunping, FU Qiang, XU Yan. Real-time Superpixels Based Tracking Method[J]. Journal of Electronics & Information Technology, 2016, 38(3): 571-577. doi: 10.11999/JEIT150705

WANG Wei, WANG Chunping, FU Qiang, XU Yan. Real-time Superpixels Based Tracking Method[J]. Journal of Electronics & Information Technology, 2016, 38(3): 571-577. doi: 10.11999/JEIT150705

Citation:

WANG Wei, WANG Chunping, FU Qiang, XU Yan. Real-time Superpixels Based Tracking Method[J]. Journal of Electronics & Information Technology, 2016, 38(3): 571-577. doi: 10.11999/JEIT150705

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Real-time Superpixels Based Tracking Method

doi: 10.11999/JEIT150705

Funds:

The National Natural Science Foundation of China (61141009)

Received Date: 2015-06-08
Rev Recd Date: 2015-12-04
Publish Date: 2016-03-19

Abstract

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.
- Visual tracking,
- Superpixels,
- Discriminative appearance model,
- K-Nearest Neighbor (KNN)

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References(20)

References

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