Deep Ranking for Person Re-identification via Joint Representation Learning

Understand

This paper proposes a novel approach to person re-identification, a fundamental task in distributed multi-camera surveillance systems.

• Although a variety of powerful algorithms have been presented in the past few years, most of them usually focus on designing hand-crafted features and learning metrics either individually or sequentially.
• Different from previous works, we formulate a unified deep ranking framework that jointly tackles both of these key components to maximize their strengths.
• We start from the principle that the correct match of the probe image should be positioned in the top rank within the whole gallery set.