基于大边距度量学习的车辆再识别方法

Large margin metric learning based vehicle re-identification method

在目前的车辆再识别方法中,车辆在拍摄过程中的多视角会导致特征嵌入空间中决策边界附近样本较难区分.针对该问题,提出通过最大化边界距离提升车辆再识别准确率.在特征表示阶段,设计了大边界损失度量函数,可以有效处理相似车辆的混淆问题;采用入侵叛逃采样策略,可以在训练样本中找出更容易混淆的难样本以更有针对性地训练网络,并加快网络的训练速度.在车辆检索阶段,提出基于核函数的重排序方法,可以提高车辆再识别的准确率.在3个公共数据集上的实验结果显示,车辆再识别的准确率得到提高,同时训练和推理效率得到改善.理论分析和实验表明,大边距度量学习通过挖掘决策边界的难样本,可以有效解决车辆再识别中的多视角问题.

In the present vehicle re-identification methods,it is hard to separate the samples near the decision boundaries in the embedding feature space because of the multi-view vehicle appearance.A large margin metric learning method was proposed to tackle the above problem.In the representation stage,a large margin loss function was presented to tackle the identity confusion issue among similar vehicles effectively.Meanwhile,an invaderdefector sampler was adopted,which was used to find the hard samples in the training dataset to train the network specifically and speed up the training process.A kernelized re-rank method was adopted to further enhance the reidentification performance in the retrieval stage.Experiments on three common databases show that the proposed method can achieve a higher vehicle re-identification accuracy,and at the same time the time cost of training and the inference procedure are improved.Theoretical analysis and experiments also indicate that,the large margin metric learning method can mine the hard samples near the decision boundaries,and can solve the multi-view issue in the vehicle re-identification domain.