CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification

Abstract

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 sce-narios. 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 rele-vant neighbors, which reduces the reliability of the neigh-bors by introducing intra-camera negative samples and ex-cluding inter-camera positive samples. To overcome this problem, we propose a novel camera-aware Jaccard (CA-Jaccard) distance that leverages camera information to en-hance the reliability of Jaccard distance. Specifically, we design camera-aware k-reciprocal nearest neighbors (CK-RNNs) 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 con-tribution of inter-camera samples in the overlap. More-over, 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 as-sign these samples higher weights in overlap, further im-proving 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 ef-fectiveness of our method. Code is available at https://github.com/chen960/CA-Jaccard/.