Person Re-Identification Based on Partition Adaptive Network Structure and Channel Partition Weight Adaptive
Wenjie Chen, Fan Yang · IEEE Access · 2021
This article noticed that the feature of the block in human body image is noticeable, some algorithms in pedestrian re-identification are based on partitioning the human body to calculate the similarity between pedestrians images. However, it is not easy to find a proper method to partition the pedestrian image. This article proposes a method partition adaptive network structure (PANS) to automatically determine the partition scheme. This method can save much work to find a suitable partitioning scheme, and the effect of the automatic partitioning scheme is better than that of the manual partitioning scheme. In addition, this paper proposes a pedestrian re-recognition method based on channel partition weight adaptive (CPWA). We obtained better results on three public pedestrian re-identification data sets compared with the baseline. This method, combined with the automatic partitioning scheme proposed in this article, can improve results. We have done experiments on the three public data sets of market-1501, DukeMTMC-Reid, and CUHK03, proving the superiority of the two methods proposed in this article.