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Academic Journal of Computing & Information Science, 2019, 2(3); doi: 10.25236/AJCIS.020311.

Object Tracking Based on Multi-Feature Fusion and Kalman Filter


Yibo Min, Jianwei Ma* and Shaofei Zang

Corresponding Author:
Jianwei Ma

School of Information Engineering, Henan University of Science and Technology Luoyang, China
*Corresponding author e-mail: yibo_min@163.com


Aiming at the problem of tracking drift caused by the variation of illumination variation, deformation and occlusion problems in the target tracking process, a traditional Mean Shift algorithm is proposed. Firstly, LBP and LPQ are used to perform texture feature extraction on the target image, and the extracted color features and texture features are weighted and fused. Secondly, a target tracking method combining kalman filter and mean shift tracking is proposed. The scale adaptive mechanism is used to effectively improve the tracking stability of mean shift when the target scale changes. Experiments show that the improved algorithm is more robust to target deformation, illumination, and occlusion.


Texture feature, Object Tracking, Kalman filter, Mean shift

Cite This Paper

Yibo Min, Jianwei Ma and Shaofei Zang. Object Tracking Based on Multi-Feature Fusion and Kalman Filter. Academic Journal of Computing & Information Science (2019), Vol. 2, Issue 3: 92-103. https://doi.org/10.25236/AJCIS.020311.


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