Person re-identification (re-ID) is a challenging task that aims to learn discriminative features for person retrieval. In person re-ID, Jaccard distance is a widely used distance metric, especially in re-ranking and clustering scenarios. However, we discover that camera variation has a significant negative impact on the reliability of Jaccard distance. In particular, Jaccard distance calculates the distance based on the overlap of relevant neighbors. Due to camera variation, intra-camera samples dominate the relevant neighbors, which reduces the reliability of the neighbors by introducing intra-camera negative samples and excluding inter-camera positive samples. To overcome this problem, we propose a novel camera-aware Jaccard (CA-Jaccard) distance that leverages camera information to enhance the reliability of Jaccard distance. Specifically, we introduce camera-aware k-reciprocal nearest neighbors (CKRNNs) to find k-reciprocal nearest neighbors on the intra-camera and inter-camera ranking lists, which improves the reliability of relevant neighbors and guarantees the contribution of inter-camera samples in the overlap. Moreover, we propose a camera-aware local query expansion (CLQE) to exploit camera variation as a strong constraint to mine reliable samples in relevant neighbors and assign these samples higher weights in overlap to further improve the reliability. Our CA-Jaccard distance is simple yet effective and can serve as a general distance metric for person re-ID methods with high reliability and low computational cost. Extensive experiments demonstrate the effectiveness of our method.

翻译：摘要：行人重识别（re-ID）是一项具有挑战性的任务，旨在学习用于行人检索的判别性特征。在行人重识别中，Jaccard距离是一种广泛使用的距离度量，尤其在重排序和聚类场景中。然而，我们发现相机变化对Jaccard距离的可靠性有显著的负面影响。具体而言，Jaccard距离基于相关邻居的重叠程度计算距离。由于相机变化，同相机样本主导了相关邻居，这通过引入同相机负样本并排除跨相机正样本，降低了邻居的可靠性。为解决此问题，我们提出了一种新颖的相机感知Jaccard（CA-Jaccard）距离，利用相机信息增强Jaccard距离的可靠性。具体地，我们引入相机感知k倒排最近邻（CKRNNs），在同相机和跨相机排序列表中寻找k倒排最近邻，从而提升相关邻居的可靠性，并确保跨相机样本在重叠中的贡献。此外，我们提出相机感知局部查询扩展（CLQE），将相机变化作为强约束来挖掘相关邻居中的可靠样本，并为这些样本在重叠中分配更高权重，以进一步提高可靠性。我们的CA-Jaccard距离既简单又有效，可作为行人重识别方法通用距离度量，具有高可靠性和低计算成本。大量实验证明了我们方法的有效性。