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| Optimal Matching Tracking Algorithm Based on Discriminant Appearance Model |
| LIU Wanjun1,2 , LIU Daqian2, FEI Bowen3 |
1.School of Software, Liaoning Technical University, Huludao 125105
2.School of Electronics and Information Engineering, Liaoning Technical University, Huludao 125105
3.School of Business Administration, Liaoning Technical University, Huludao 125105 |
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Abstract Traditional model matching and tracking algorithms are easily influenced by the occlusion of other targets and the complex background. To solve these problems, an optimal matching tracking algorithm based on discriminant appearance model is proposed. Firstly, the local feature blocks of the previous 5 frames of the image sequences are extracted by sampling, and the training sample set consisting of a number of feature blocks is established. Then, the feature blocks with the same color and texture features are clustered to build a discriminant appearance model. Secondly, the bi-directional optimal similarity matching method is adopted for target detection. To avoid complex background interference, a method of foreground partition is proposed to acquire more accurate matching results. Finally, the tracking results are periodically added to the clustering collection to update the appearance model. The experimental results indicate that the proposed approach provides higher tracking accuracy under the conditions of partial occlusion and complex background by using the discriminant appearance model of multi-frame training and the bi-directional optimal similarity matching method.
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Received: 13 March 2017
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About author:: (LIU Wanjun, born in 1959, master, professor. His research interests include image and vision computing, target detection and tracking.)
(LIU Daqian(Corresponding author), born in 1992, Ph.D. candidate. His research interests include image and vision computing, target detection and tracking.)
(FEI Bowen, born in 1991, Ph.D. candidate. Her research interests include data mi-ning and intelligent data processing.) |
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2017, Vol. 30 
