关键词: 摄像头风格学习, triplet loss, 行人重识别, 多粒度损失

Abstract:

Person re-identification is a hot topic in computer community and has many applications, such as transportation, public safety and video surveillance. This paper proposes a new method, which incorporates Camera Style Learning（CSL） and Multi-Granularity Loss（MGL）, has achieved state-of-the-art results in person re-ID domain. CSL can reduce the impact caused by the difference of camera style, which can preferably play the advantage of triplet loss and effectively improve the recognition accuracy. MGL is adopted in the learning process, by which multiple levels of feature maps are exploited to make the learned features more discriminate. Experiments on two large-scale datasets, Market-1501 and DukeMTMC-reID, show the proposed method obtains 3.7% and 3.2% improvement in Rank-1 precision over the baseline Camstyle and the results reach 93.2% and 81.5%, respectively. On the Market-1501 dataset, this paper achieves 93.8% in mAP and 96.1% in Rank-1 accuracy after re-ranking, after MGL is applied, surpassing the state of the art.

Key words: camera style learning, triplet loss, person re-identification, multi-granularity loss