A Mask-Pooling Model With Local-Level Triplet Loss for Person Re-Identification
Fudan Zheng, Tingting Cai, Ying Wang, Chufu Deng, Zhiguang Chen, Huiling Zhu · IEEE Access · 2020
Person Re-Identification (ReID) is an important yet challenging task in computer vision. Background clutter is one of the greatest challenges to overcome. In this paper, we propose a Mask-pooling model with local-level triplet loss (MPM-LTL) to tackle this problem and improve person ReID performance. Specifically, we present a novel pooling method, called mask pooling (MP), to gradually remove background features in feature maps through deep convolutional network. With mask pooling, the network can learn the most crucial person features. Moreover, we raise a novel local-level triplet loss for discriminative feature training. Furthermore, we propose a new hard triplets selection algorithm named Mask-guided TriHard. The method is based on human outline information, which is, to our best knowledge, employed for the first time for hard triplets selection. We achieve the state-of-the-art results on three benchmark person datasets Market-1501 [1], CUHK03 [2] and DukeMTMC-reID [3], [4].