Person re-identification from appearance cues and deep Siamese features

Abstract

Automated person re-identification in a multi-camera surveillance setup is very important for effective tracking and monitoring crowd movement. In this paper, we propose an efficient hierarchical re-identification approach in which color histogram-based comparison is employed to find the closest matches in the gallery set, and next deep feature-based comparison is carried out using the Siamese network. Reduction in search space after the first level of matching helps in improving the accuracy as well as efficiency of prediction by the Siamese network by eliminating dissimilar elements. A silhouette part-based feature extraction scheme is adopted in each level of hierarchy to preserve the relative locations of the different body parts and make the appearance descriptors more discriminating. The proposed approach has been evaluated on five public data sets and also a new data set captured in our laboratory. Results reveal that it outperforms most state-of-the-art approaches in terms of overall accuracy.