Person Re-identification Using Partial Least Squares Appearance Modeling

Due to the large areas covered by surveillance systems, employed cameras usually lack intersection of field of view, refraining us from mapping the location of a person in a camera to another one. Therefore, when a subject appears in a camera, a person re-identification method is required to discover whether the subject has been previously identified in a different camera. Even though several approaches have been proposed in the literature, person re-identification is still a challenging problem due to appearance variation between cameras, changes in illumination, pose variation, and low quality data, among others. To reduce the effect of the aforementioned difficulties, we propose a person re-identification approach that models the appearance of the subjects based on multiple samples collected from multiple cameras and employs person detection and tracking to enhance the robustness of the method. Experiments conducted on three public available data sets demonstrate improvements over existing methods.

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