A Method of Pedestrian Re-identification Based on Multiple Saliency Features
In the field of pedestrian re-identification, the appearance of pedestrians usually varies greatly in different cameras as the pedestrians in the sequences of surveillance video may be affected by the changes of the visual angles, the postures and the light. According to this, a method of pedestrian re-identification based on multiple salient features is proposed. The traditional method of pedestrian re-identification based on salient features characterizes the weight through the difference between different samples. However, the calculation result of the traditional way is not stable enough and may vary with the rich diversity of samples. Therefore, a method based on the cellular automata is used to calculate the inherently salient features of pedestrian images. In order to make full use of the advantages of the above methods, we introduce the multi-layer cellular automata to fuse them to achieve better results in the experiment. The experimental results show that the proposed algorithm has better performance on CAVIAR4REID and iLIDS databases than the existing algorithms.
KeywordsPedestrian re-identification Fusion of multiple salient features Multi-layer cellular automata
The authors would like to thank the providers for their open pedestrian datasets. This work is supported by Scientific Research Foundation of China (61402237).
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