Gait Cycle Reconstruction and Human Identification from Occluded Sequences
This work addresses a practical challenge in surveillance and biometrics by improving gait recognition under occlusion, though it is incremental as it builds on existing neural network techniques.
The paper tackles the problem of gait-based person identification from occluded video sequences by proposing a neural network model to reconstruct occluded frames before recognition, achieving superior performance over existing methods as verified through Dice scores and recognition accuracy on datasets like OU-ISIR LP, CASIA-B, and TUM-IITKGP.
Gait-based person identification from videos captured at surveillance sites using Computer Vision-based techniques is quite challenging since these walking sequences are usually corrupted with occlusion, and a complete cycle of gait is not always available. In this work, we propose an effective neural network-based model to reconstruct the occluded frames in an input sequence before carrying out gait recognition. Specifically, we employ LSTM networks to predict an embedding for each occluded frame both from the forward and the backward directions, and next fuse the predictions from the two LSTMs by employing a network of residual blocks and convolutional layers. While the LSTMs are trained to minimize the mean-squared loss, the fusion network is trained to optimize the pixel-wise cross-entropy loss between the ground-truth and the reconstructed samples. Evaluation of our approach has been done using synthetically occluded sequences generated from the OU-ISIR LP and CASIA-B data and real-occluded sequences present in the TUM-IITKGP data. The effectiveness of the proposed reconstruction model has been verified through the Dice score and gait-based recognition accuracy using some popular gait recognition methods. Comparative study with existing occlusion handling methods in gait recognition highlights the superiority of our proposed occlusion reconstruction approach over the others.