Weighted triple-sequence loss for video-based person re-identification

Person re-identification (re-id) task has attracted a lot of attention because of the excellent performance powered by the Convolutional Neural Network (CNN). However, video-based person re-id is still challenging and far to be solved. On the one hand, the sequence contains complementary information but also more noise information. On the other hand, the training for video is still mainly based on classifying a single frame via its person identity. The representation of the video is generated by aggregating each frame feature. All these will cause that the robustness of video feature is not adequate at the training stage, and the model is easy to be misled by noise information existing in frames. In order to alleviate the difficulty of training video-based re-id, we propose a novel loss named Weighted Triple-Sequence Loss (WTSL) to optimize the video-based feature and reduce the impact of outliers. Further more, we design a Spatial Transformed Partial Network (STPN) coordinated with jointly optimizing image-level and video-level features to generate more robust representation. Extensive experiments show that our algorithm outperforms the state-of-the-art results and achieves 82.2%, 95.2%, and 85.9% rank-1 accuracy on three popular video-based benchmarks: iLIDS-VID, PRID2011, and MARS, respectively.