  Automated person re-identification in a multi-camera surveillance setup is
very important for effective tracking and monitoring crowd movement. In the
recent years, few deep learning based re-identification approaches have been
developed which are quite accurate but time-intensive, and hence not very
suitable for practical purposes. In this paper, we propose an efficient
hierarchical re-identification approach in which color histogram based
comparison is first employed to find the closest matches in the gallery set,
and next deep feature based comparison is carried out using Siamese network.
Reduction in search space after the first level of matching helps in achieving
a fast response time as well as improving the accuracy of prediction by the
Siamese network by eliminating vastly 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 structures and make the
appearance descriptors more discriminating in nature. The proposed approach has
been evaluated on five public data sets and also a new data set captured by our
team in our laboratory. Results reveal that it outperforms most
state-of-the-art approaches in terms of overall accuracy.
