  Person re-identification is vital for monitoring and tracking crowd movement
to enhance public security. However, re-identification in the presence of
occlusion substantially reduces the performance of existing systems and is a
challenging area. In this work, we propose a plausible solution to this problem
by developing effective occlusion detection and reconstruction framework for
RGB images/videos consisting of Deep Neural Networks. Specifically, a CNN-based
occlusion detection model classifies individual input frames, followed by a
Conv-LSTM and Autoencoder to reconstruct the occluded pixels corresponding to
the occluded frames for sequential (video) and non-sequential (image) data,
respectively. The quality of the reconstructed RGB frames is further refined
and fine-tuned using a Conditional Generative Adversarial Network (cGAN). Our
method is evaluated on four well-known public data sets of the domain, and the
qualitative reconstruction results are indeed appealing. Quantitative
evaluation in terms of re-identification accuracy of the Siamese network showed
an exceptional Rank-1 accuracy after occluded pixel reconstruction on various
datasets. A comparative analysis with state-of-the-art approaches also
demonstrates the robustness of our work for use in real-life surveillance
systems.
