Deep Metric Learning with Symmetric Triplet Constraint for Person Re identification
Deep metric learning is an effective method for person re identification (PR ID) In practice, impostor samples generally own more discriminative information than other well separable negative samples (WSN samples) Specifically, existing triplet based deep learning methods with asymmetric triplet constraint can not effectively remove impostors because they treat all different types of negative samples equally. To utilize discriminative information in negative samples more efficiently, we build a symmetric triplet constraint based deep metric learning network (STDML) STDML designs a symmetric triplet margin maximized objective function, which requires impostors to leave more than a margin from positive pair and requires the distance between WSN samples maximized, simultaneously. Experiments on three benchmark datasets (CUHK03, CUHK01 and VIPeR) achieve better performance than existing methods and significantly improved the effectiveness of exploiting discriminative information from WSN samples.
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