  Recent advancements in computational resources and Deep Learning
methodologies has significantly benefited development of intelligent
vision-based surveillance applications. Gait recognition in the presence of
occlusion is one of the challenging research topics in this area, and the
solutions proposed by researchers to date lack in robustness and also dependent
of several unrealistic constraints, which limits their practical applicability.
We improve the state-of-the-art by developing novel deep learning-based
algorithms to identify the occluded frames in an input sequence and next
reconstruct these occluded frames by exploiting the spatio-temporal information
present in the gait sequence. The multi-stage pipeline adopted in this work
consists of key pose mapping, occlusion detection and reconstruction, and
finally gait recognition. While the key pose mapping and occlusion detection
phases are done %using Constrained KMeans Clustering and via a graph sorting
algorithm, reconstruction of occluded frames is done by fusing the key
pose-specific information derived in the previous step along with the
spatio-temporal information contained in a gait sequence using a Bi-Directional
Long Short Time Memory. This occlusion reconstruction model has been trained
using synthetically occluded CASIA-B and OU-ISIR data, and the trained model is
termed as Bidirectional Gait Reconstruction Network BGait-R-Net. Our LSTM-based
model reconstructs occlusion and generates frames that are temporally
consistent with the periodic pattern of a gait cycle, while simultaneously
preserving the body structure.
