A jointly learned deep embedding for person re-identification

Abstract

Person re-identification (ReID) is an importance study issue in the modern video surveillance area. However, it is very challenging due to the large variations of intra-class and the small variations of inter-class. To solve the problem, most deep ReID methods usually learn a feature embedding based on verification or identification. However, both learning approaches are complementary and neither of them can well address this issue. Motivated by this, we propose a deep joint learning framework based on verification-identification to achieve a discriminative deep feature embedding. Specifically, for verification, a triplet network is adopted and an improved triplet loss is proposed by adding a novel penalty item in the framework. For identification, an identity classifier with the softmax loss is attached to the top of the triplet network at the same time. Thus, driven by the improved triplet loss and the softmax loss simultaneously, a more discriminative and compact feature embedding will be learned for ReID. Extensive experiments over several popular benchmarks achieve state-of-the-art results which well demonstrate the effectiveness of the proposed method for ReID.