Vehicle Re-Identification: Logistic Triplet Embedding Regularized by Label Smoothing

The explosive increasing of vehicles cause amount of traffic problems. Although vehicle re-identification (Re-ID) can help to acquire and manage vehicles, some intrinsic difficulties hinder the application of vehicle Re-ID. For example, vehicles have little inter-instance discrepancy due to their rigid structures and finite models. To address this problem, in this paper, a logistic triplet loss is proposed to fuse a label-smoothing cross entropy to extract fine-grained feature embeddings. Via exploring deeper into the inter-instance variances, the novel loss combines advantages of classification and metric learning, and reveals more stable performance than popular triplet loss. The experimental results on public datasets demonstrate the effectiveness of the proposed loss compared with state-of-the-art approaches.

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