CA-Jaccard：面向摄像头的Jaccard距离用于人重识别
CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification
AI总结：
CA-Jaccard通过引入摄像头信息提升人重识别中Jaccard距离的可靠性，有效解决摄像头变化带来的负面影响。
摘要：
人重识别（re-ID）是一项具有挑战性的任务，旨在学习用于人检索的判别特征。在人重识别中，Jaccard距离是一种广泛使用的距离度量方法，尤其是在重新排序和聚类场景中。然而，我们发现摄像头变化对Jaccard距离的可靠性有显著的负面影响。特别是，Jaccard距离基于相关邻居的重叠来计算距离。由于摄像头变化，同一摄像头样本主导了相关邻居，这通过引入同一摄像头的负样本和排除跨摄像头的正样本而降低了邻居的可靠性。为了解决这个问题，我们提出了一种新的面向摄像头的Jaccard（CA-Jaccard）距离，利用摄像头信息来增强Jaccard距离的可靠性。具体来说，我们设计了面向摄像头的k-反向最近邻（CKRNNs），以在同摄像头和跨摄像头的排序列表上找到k-反向最近邻，从而提高相关邻居的可靠性并保证重叠中跨摄像头样本的贡献。此外，我们提出了一种面向摄像头的局部查询扩展（CLQE），通过利用摄像头变化作为强约束来挖掘相关邻居中的可靠样本，并将这些样本在重叠中赋予更高的权重，进一步提高可靠性。我们的CA-Jaccard距离简单而有效，可以作为具有高可靠性和低计算成本的人重识别方法的通用距离度量方法。广泛的实验验证了我们方法的有效性。
英文摘要：
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 design 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 mine reliable samples in relevant neighbors by exploiting camera variation as a strong constraint and assign these samples higher weights in overlap, further improving 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
