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| Person Re identification Based on Multi feature Fusion |
| YUAN Li, TIAN Ziru |
| School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083 |
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Abstract Due to variations in pose and illumination condition, the appearance of a person can be significantly different in two views and therefore the performance of person re identification is degraded. In this paper, a feature fusion method for person re identification is proposed including HSV color feature， color histogram feature and texture feature extracted by the histogram of oriented gradient descriptor. The specific process is divided into the training phase and the recognition phase. In the training phase, the feature descriptors of each image in the reference dataset are firstly extracted, and then a correlation matrix of the image features from two cameras is learned using canonical correlation analysis. As for re identification, the feature descriptors of each image in the gallery dataset and the probe dataset are firstly extracted, and then they are transformed by the correlation matrix. Finally， re identification is implemented by measuring the similarity between the gallery image descriptor and the probe image descriptor. Experimental results on three datasets show that the proposed method outperforms the state of art approaches.
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Received: 04 August 2016
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| Fund:Supported by National Natural Science Foundation of China(No.61300075) |
About author:: YUAN Li(Corresponding author), born in 1978, Ph.D., associate professor. Her research interests include pattern recognition and image processing.
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TIAN Ziru, born in 1990, master student. Her research interests include pattern recognition and image processing. |
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2017, Vol. 30 
