Improved Kernel Correlation Filtering Tracking for Vehicle Video

HUANG Liqin; ZHU Piao

doi:10.11999/JEIT171109

Volume 40 Issue 8

Aug. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(8): 1887-1894

HUANG Liqin, ZHU Piao. Improved Kernel Correlation Filtering Tracking for Vehicle Video[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1887-1894. doi: 10.11999/JEIT171109

Citation:

HUANG Liqin, ZHU Piao. Improved Kernel Correlation Filtering Tracking for Vehicle Video[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1887-1894. doi: 10.11999/JEIT171109

HUANG Liqin, ZHU Piao. Improved Kernel Correlation Filtering Tracking for Vehicle Video[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1887-1894. doi: 10.11999/JEIT171109

Citation:

HUANG Liqin, ZHU Piao. Improved Kernel Correlation Filtering Tracking for Vehicle Video[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1887-1894. doi: 10.11999/JEIT171109

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Improved Kernel Correlation Filtering Tracking for Vehicle Video

doi: 10.11999/JEIT171109

Funds:

The National Natural Science Foundation of China (61471124), The Major Science and Technology Projects in Fujian Proviuce (2017H6009, 2018H0018), The Cernet Innovation Projects (NGII20160208, NGII20170201)

Received Date: 2017-11-27
Rev Recd Date: 2018-04-18
Publish Date: 2018-08-19

Abstract

Abstract

For videos captured by in-car cameras, the filter-based tracking is a challenging task due to complex environments and mutable object scales. A scale adaptive tracking filter is proposed based on the background information. Firstly, the relative motion of each object is estimated by extracting features from gradient histograms between frames. Then, the object location on the next frame is determined and utilized to delimit an image block. Finally, the object scale is obtained through dynamic scaling pyramid model within image block. The proposed algorithm is examined by 27 in-car videos including 23 KITTI videos and 4 domestic videos. In experiments, the proposed algorithm suppresses effectively the interferences of environments and objects. It achieves more accurate and more robust object tracking than several popular benchmarks including KCF, DSST, SAMF, SATPLE.
- Object tracking,
- Kernelized correlation filters,
- Videos captured by in-car cameras,
- Context aware,
- Scale estimation

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References(24)

References

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