HMMN: Online metric learning for human re-identification via hard sample mining memory network

Abstract

Effective metric learning is important in various applications, especially for re-identification. Compared with most existing re-identification methods which are not suitable for a real-time update mode, we exploit a novel memory-based strategy for mining hard triplets in online metric learning. This strategy is realized with an end-to-end deep learning based framework using an external memory pool. Our proposed pipeline is able to explicitly provide hard negative and positive samples to generate effective triplets, which are important for online metric learning due to the representative triplets could provide distinctive information to help understand the concept of metric learning between categories. In addition, a “focal-triplet loss” function is proposed to deal with the lack of positive or negative samples for one anchor, and the imbalance between easy and hard triplets for mini-batch. Experimental results on Market-1501, CUHK03 and DukeMTMC-reID demonstrate the effectiveness of our method, and its performance even outperforms that of some existing offline methods.