In recent years

a number of surveillance cameras have been placed. The number of surveillance videos that need to be observed and analyzed is rising rapidly because of increased traffic road monitoring and indoor surveillance needs. Single cameras are insufficient to meet the need of certain monitoring tasks because of the significant increase in the number of outdoor cameras. The multi-camera-based intelligent analysis of human behavior is increasingly valuable in the field of video surveillance

and multi-camera-based pedestrian re-identification is a significant research direction. This paper proposes a new method of pedestrian re-identification based on feature transformation and dataset classification. In this method

a new pedestrian re-identification algorithm is proposed based on the transformation matrix that can massively eliminate the differences of the features. This algorithm can be used in high-dimensional space

in which a vector approaches another vector through the transformation matrix.Therefore

this algorithm is capable of eliminating the feature differences of the same pedestrian. This paper also proposes a new algorithm based on the pedestrian feature classification of datasets; the pedestrian features of each class have similar properties.Thus

these features can share the same transformation matrix

and the algorithm can eliminate the feature differences of a pedestrian under multi-camera. In detail

blocks are built by clustering based on block features. The transformation matrix in each block is trained based on the multi-channel features under different cameras. Each block has a corresponding transformation matrix.The correct block for each testing pedestrian can be determined by comparing that block to that of the training pedestrians. Thus

the transformation matrix of the chosen block is the appropriate matrix. The chosen transformation matrix then eliminates the differences in the features of the testing pedestrian under multi-camera. Experimental results prove that the proposed method can improve the accuracy of finding the same pedestrian under multi-camera. Specifically

the Rank1 matching rate of the method in VIPeR dataset (50% training

50% testing) is 22%

which is better than the results of other existing methods. The feature transformation module and the dataset classification module can match the same pedestrian under multi-camera. The matching rate of pedestrian can be reduced significantly if one of the two modules is eliminated. Moreover

the experiments are designed to verify the robustness characteristics of the chosen feature. The experimental results based on real street scene dataset show that the chosen feature is more out standing and robust than some of the existing features. This paper proposes a new method of pedestrian re-identification based on feature transformation and dataset classification. An appropriate transformation matrix can eliminate the influences of illumination and pedestrian poses under multi-camera. Thus

the method can make the features of the same pedestrian close to each other. The method can also significantly improve the accuracy of matching pedestrians under multi-camera.