An efficient global representation constrained by Angular Triplet loss for vehicle re-identification

Abstract

Vehicle re-identification is becoming an increasingly important problem in modern intelligent transportation systems. Substantial results have been achieved with methods based on deep metric learning. Most of the previous works tend to design complicated neural network models or utilize extra information. In this work, we introduce a simple Angular Triplet loss on the basis of analysis of different feature representations constrained by softmax loss and triplet loss. A batch normalization layer with zero bias is adopted to pass through the embedded feature before loss calculation. Then, triplet loss is calculated in cosine metric space instead of Euclidean space. In this way, triplet loss can cooperate with softmax consistently. By unifying the metric space of these two types of losses, the proposed method achieves 77.3% and 95.9% in rank-1 on VehicleID and VeRi-776 datasets, respectively. With only global features utilized, the proposed model can be seen as an effective baseline for vehicle re-identification task.