Incremental Re-identification by Cross-Direction and Cross-Ranking Adaption
Incremental Re-identification by Cross-Direction and Cross-Ranking Adaption
复制标题
通过交叉方向和交叉排名适应进行增量重新识别
DOI：
10.1109/tmm.2019.2898753
复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Shin'ichi Satoh
中科院分区：
文献类型：
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作者：
Zheng Wang;Junjun Jiang;Yi Yu;Shin'ichi Satoh
人物再识别在视频监控和刑侦中有着广泛的应用。为了获得更好的性能，通常会利用额外的重新排序步骤。相关方法尝试根据每个查询独立地优化结果。然而，在实际场景中，随着调查过程的进行，其他查询，特别是逐渐积累的日志，可以用来指导或规范当前的查询。在本文中，我们建议不仅根据当前查询本身，还根据其他查询和历史日志对结果进行优化。我们分别研究了不同查询之间的交叉方向约束和交叉排序约束。在此基础上，提出了一种互惠优化方法，对多个排序列表进行互惠优化。在VIPeR、新协议CUHK03和Market-1501数据集上的实验验证了我们方法的有效性。特别是在Market-1501数据集上，在充分利用其他查询的情况下，该方法在rank-1上的准确率达到了94.66%，mAP达到了非常高的75.12%，明显优于最先进的方法。
Person re-identification is widely applied in video surveillance and criminal investigation applications. To achieve better performance, an additional re-ranking step is often exploited. Related methods attempt to optimize the result according to every single query independently. However, in a practical scene, as the investigation process goes on, the other queries, in particular, the gradually accumulated logs, can be used to guide or regularize the current query. In this paper, we propose to optimize the result according to not only the current query itself but also the other queries and historical logs. We respectively investigate the cross-direction and the cross-ranking constraints among different queries. Based on the investigations, we propose a reciprocal optimization method to refine multiple ranking lists reciprocally. Experiments on the VIPeR, new-protocol CUHK03, and Market-1501 datasets confirm the effectiveness of our method. In particular, on the Market-1501 dataset, with full utilization of the other queries, the method achieves an accuracy rate of 94.66% at rank-1 and a very high mAP of 75.12%, and significantly outperforms the state-of-the-art methods.