Reinforced Temporal Attention and Split-Rate
Transfer for Depth-Based Person
Re-Identification
Abstract. We address the problem of person re-identification from commodity depth sensors. One challenge for depth-based recognition is data
scarcity. Our first contribution addresses this problem by introducing
split-rate RGB-to-Depth transfer, which leverages large RGB datasets
more effectively than popular fine-tuning approaches. Our transfer scheme
is based on the observation that the model parameters at the bottom
layers of a deep convolutional neural network can be directly shared
between RGB and depth data while the remaining layers need to be
fine-tuned rapidly. Our second contribution enhances re-identification
for video by implementing temporal attention as a Bernoulli-Sigmoid
unit acting upon frame-level features. Since this unit is stochastic, the
temporal attention parameters are trained using reinforcement learning.
Extensive experiments validate the accuracy of our method in person
re-identification from depth sequences. Finally, in a scenario where subjects wear unseen clothes, we show large performance gains compared to
a state-of-the-art model which relies on RGB data