吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (1): 281-287.doi: 10.13229/j.cnki.jdxbgxb201701041

• Orginal Article • Previous Articles     Next Articles

Novelty fragments-based target tracking with multiple instance learning under occlusions

CAI Hua1, CHEN Guang-qiu1, LIU Guang-wen1, CHENG Shuai1, YU Hua-dong2   

  1. 1.School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022,China;
    2.College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun 130022,China
  • Received:2016-02-21 Online:2017-01-20 Published:2017-01-20

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Abstract: To solve the problem that tracking algorithm may lead to drift or failure due to the accumulated error under the occlusion environment, a Multiple instance learning based Fragment Tracker (MFT) is proposed. In this MFT, the random ferns is used as the basic detector. To improve the adaption of the target change and the precision of the learning, the multiple instance learning is introduced to online update the detector. The object is segmented into fragments and parts of them are selected as the candidate set. The candidate block is tracked by the corresponding detector. The object can be finally located by the selected blocks. A real-time valid detection is made for the candidate blocks and the invalid block is replaced with an appropriate block to improve the robustness of the tracking. Experiments on variant challenging image sequence in the occlusion environment were carried out. Results show that, compared with the state-of-art trackers, the proposed MFT solves the problem of target drift and failure efficiently and has higher accuracy and better robust.

Key words: information processing, random ferns detector, multiple instance learning, fragment, invalid block replacement

CLC Number: 

  • TP391.41
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