Body structure based triplet Convolutional Neural Network for person re-identification

Person re-identification remains a challenging problem due to large variations of poses, occlusions, illumination and camera views. To learn both feature representation and similarity metric simultaneously, deep metric learning methods using triplet convolutional neural network have been applied in person re-identification. In this paper, we propose a body structure based triplet convolutional neural network (BSTCNN) for person re-identification. Specifically, a four-branch CNN architecture is built to learn features from different body parts. Body-part features are then fused in score level with a novel weighted distance layer which learns weights for each body part. We further design an improved triplet loss function to speed up convergence and boost the performance. Experimental results on two challenging datasets (CUHK01 and PRID2011) demonstrate that our approach significantly outperforms the state-of-the-art methods.