Deep feature learning for person re-identification in a large-scale crowdsourced environment

Finding the same individual across cameras in disjoint views at different locations and times, which is known as person re-identification (re-id), is an important but difficult task in intelligent visual surveillance. However, to build a practical re-id system for large-scale and crowdsourced environments, the existing approaches are largely unsuitable because of their high model complexity. In this paper, we present a deep feature learning framework for automated large-scale person re-id with low computational cost and memory usage. The experimental results show that the proposed framework is comparable to the state-of-the-art methods while having low model complexity.