Multi-model Real-time Compressive Tracking

Jianming ZHANG; Xiaokang JIN; Honglin WU; You WU

doi:10.11999/JEIT171128

Volume 40 Issue 10

Sep. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(10): 2373-2380

Jianming ZHANG, Xiaokang JIN, Honglin WU, You WU. Multi-model Real-time Compressive Tracking[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2373-2380. doi: 10.11999/JEIT171128

Citation:

Jianming ZHANG, Xiaokang JIN, Honglin WU, You WU. Multi-model Real-time Compressive Tracking[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2373-2380. doi: 10.11999/JEIT171128

Jianming ZHANG, Xiaokang JIN, Honglin WU, You WU. Multi-model Real-time Compressive Tracking[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2373-2380. doi: 10.11999/JEIT171128

Citation:

Jianming ZHANG, Xiaokang JIN, Honglin WU, You WU. Multi-model Real-time Compressive Tracking[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2373-2380. doi: 10.11999/JEIT171128

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Multi-model Real-time Compressive Tracking

doi: 10.11999/JEIT171128

1.
Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, China
2.
School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China

Funds: The National Natural Science Foundation of China (61402053, 61772454, 61811530332), The Scientific Research Fund of Hunan Provincial Education Department (16A008)

Received Date: 2017-11-30
Rev Recd Date: 2018-07-16

Available Online: 2018-07-25

Publish Date: 2018-10-01

Abstract

Abstract

Object tracking is easily influenced by illumination, occlusion, scale, background clutter, and fast motion, and it requires higher real-time performance. The object tracking algorithm based on compressive sensing has a better real-time performance but performs weakly in tracking when object appearance is changed greatly. Based on the framework of compressive sensing, a Multi-Model real-time Compressive Tracking (MMCT) algorithm is proposed, which adopts the compressive sensing to decrease the high dimensional features for the tracking process and to satisfy the real-time performance. The MMCT algorithm selects the most suitable classifier by judging the maximum classification score difference of classifiers in the previous two frames, and enhances the accuracy of location. The MMCT algorithm also presents a new model update strategy, which employs the fixed or dynamic learning rates according to the differences of decision classifiers and improves the precision of classification. The multi-model introduced by MMCT does not increase the computational burden and shows an excellent real-time performance. The experimental results indicate that the MMCT algorithm can well adapt to illumination, occlusion, background clutter and plane-rotation.
- Object tracking,
- Compressive Sensing (CS),
- Real-time,
- Multi-model,
- Dynamic learning rate

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References(16)

References

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