  Video-based person re-identification matches video clips of people across
non-overlapping cameras. Most existing methods tackle this problem by encoding
each video frame in its entirety and computing an aggregate representation
across all frames. In practice, people are often partially occluded, which can
corrupt the extracted features. Instead, we propose a new spatiotemporal
attention model that automatically discovers a diverse set of distinctive body
parts. This allows useful information to be extracted from all frames without
succumbing to occlusions and misalignments. The network learns multiple spatial
attention models and employs a diversity regularization term to ensure multiple
models do not discover the same body part. Features extracted from local image
regions are organized by spatial attention model and are combined using
temporal attention. As a result, the network learns latent representations of
the face, torso and other body parts using the best available image patches
from the entire video sequence. Extensive evaluations on three datasets show
that our framework outperforms the state-of-the-art approaches by large margins
on multiple metrics.
