Deep pixel regeneration for occlusion reconstruction in person re-identification

Abstract

Person re-identification is very important for monitoring and tracking crowd movement to provide public security. However, re-identification in the presence of occlusion is a chal lenging area that has not received significant attention yet. In this work, we propose a plausible solution to this problem by developing effective techniques for occlusion detection and reconstruction from RGB images/videos using Deep Neural Networks. Specifically, a CNN-based occlusion detection model is used to detect the occluded frames in an input sequence, following which a Conv-LSTM model or an Autoencoder is employed to recon struct the pixels corresponding to the occluded regions depending on whether the input frames are sequential or non-sequential. The quality of the reconstructed RGB frames is fur ther refined using a DCGAN. Our method has been evaluated using four public data sets for cumulative rank-based accuracy and Dice score, and the qualitative reconstruction results are indeed appealing. Quantitative evaluation in terms of re-identification accuracy using a Siamese classifier shows a Rank-1 accuracy of over 70% after reconstructing the occlu sion present in each of these datasets. A comparative study with popular state-of-the-art approaches also demonstrates the effectiveness of our work for use in real-life surveillance sites.