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Person re-identification (re-ID) is a challenging task that aims to learndiscriminative features for person retrieval. In person re-ID, Jaccard distanceis a widely used distance metric, especially in re-ranking and clusteringscenarios. However, we discover that camera variation has a significantnegative impact on the reliability of Jaccard distance. In particular, Jaccarddistance 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-cameranegative samples and excluding inter-camera positive samples. To overcome thisproblem, we propose a novel camera-aware Jaccard (CA-Jaccard) distance thatleverages 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-cameraranking lists, which improves the reliability of relevant neighbors andguarantees the contribution of inter-camera samples in the overlap. Moreover,we propose a camera-aware local query expansion (CLQE) to exploit cameravariation as a strong constraint to mine reliable samples in relevant neighborsand assign these samples higher weights in overlap to further improve thereliability. Our CA-Jaccard distance is simple yet effective and can serve as ageneral distance metric for person re-ID methods with high reliability and lowcomputational cost. Extensive experiments demonstrate the effectiveness of ourmethod.

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