IEEE Trans Cybern. 2020 Feb;50(2):561-571. doi: 10.1109/TCYB.2018.2869739. Epub 2018 Oct 9.
Person re-identification (person re-id) has attracted rapidly increasing attention in computer vision and pattern recognition research community in recent years. With the goal of providing match ranking results between each query person image and the gallery ones, the person re-id technique has been widely explored and a large number of person re-id methods have been developed. As these algorithms leverage different kinds of prior assumptions, image features, distance matching functions, et al., each of them has its own strengths and weaknesses. Inspired by these facts, this paper proposes a novel person re-id method based on the idea of inferring superior fusion results from a variety of previous base person re-id algorithms using different methodologies or features. To achieve this goal, we propose a novel framework which mainly consists of two steps: 1) a number of existing person re-id methods are implemented, and the ranking results are obtained in the test datasets. and 2) the robust fusion strategy is applied to obtain better re-ranked matching results by simultaneously considering the recognition abilities of various base re-id methods and the difficulties of different gallery person images to be correctly recognized under the generative model of labels, abilities, and difficulties framework. Comprehensive experiments show the effectiveness of our proposed method, and we have received state-of-the-art results on recent popular person re-id datasets.
近年来，人体重识别（person re-id）在计算机视觉和模式识别研究领域受到了越来越多的关注。人体重识别技术的目标是提供每个查询人体图像与图库之间的匹配排名结果，因此被广泛探索，并开发了大量的人体重识别方法。由于这些算法利用了不同的先验假设、图像特征、距离匹配函数等，因此每种方法都有其自身的优缺点。受这些事实的启发，本文提出了一种新的人体重识别方法，该方法基于从使用不同方法或特征的多种先前基础人体重识别算法中推断出优越融合结果的思想。为了实现这一目标，我们提出了一个新的框架，主要包括两个步骤：1）实现了一些现有的人重识别方法，并在测试数据集上获得了排名结果。2）应用稳健的融合策略，通过同时考虑各种基础重识别方法的识别能力以及在标签、能力和困难生成模型下正确识别不同图库人体图像的困难程度，获得更好的重新排序匹配结果。综合实验表明了我们提出的方法的有效性，并且在最近流行的人体重识别数据集上获得了最先进的结果。
