Visual tracking via temporally smooth sparse coding
Sparse representation has been popular in visual tracking recently for its robustness and accuracy. However, for most conventional sparse coding based trackers, the target candidates are considered independently between consecutive frames. This paper shows that the temporal correlation of these...
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Published: |
2015
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/107462 http://hdl.handle.net/10220/25489 http://dx.doi.org/10.1109/LSP.2014.2365363 |
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| Institution: | Nanyang Technological University |
| Summary: | Sparse representation has been popular in visual
tracking recently for its robustness and accuracy. However,
for most conventional sparse coding based trackers, the target
candidates are considered independently between consecutive
frames. This paper shows that the temporal correlation of these
frames can be exploited to improve the performance of tracking
and makes the tracker more robust to noise. Furthermore, to
improve the tracking speed, we revisit a more efficient method
for `1 norm problem, marginal regression, which can solve
the sparse coding problem more efficiently. Consequently we
can realize real-time tracking based on the temporal smooth
sparse representation. Extensive experiments have been done to
demonstrate the effectiveness and efficiency of our method. |
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