基于统计推断的行人再识别算法

A Statistical Inference Approach for Person Re-identification

行人再识别是指给定一张行人图像,在已有的可能来源于非交叠摄像机视场的行人图像库中,识别出与此人相同的图像。研究该问题有着非常重要的现实意义,同时也面临许多挑战。该文提出一种基于统计推断的行人再识别算法。该算法从统计推断的角度出发学习两幅行人图像的相似度度量函数,利用此函数从行人图像库中搜索待查询的人。在公共数据集VIPeR上的实验表明,该算法性能优于已有的行人再识别算法,学习相似度度量函数的时间花销明显少于已有的基于学习的算法,并且在只有少量训练样本时,缓解了学习相似度度量函数的过拟合问题。

Person re-identification, identifying the same person’s images in an existing database come from non-overlapping camera views, is a valuable but challenging task. This paper proposes a statistical inference approach for person re-identification. A similarity measure of two person images is learned from a statistical inference perspective. Then the similarity measure is utilized to query a person from a gallery set. The proposed approach is demonstrated on VIPeR dataset, and the experiment shows that it outperforms the state-of-the-art approaches. Besides, it costs less time than the existing learning-based ones in training, and alleviates the over-fitting problem when there are few training data.