基于能量模型的行人与车辆再识别方法

为了解决行人再识别以及车辆再识别算法中网络训练过程对计算资源的消耗过大且准确率较低的问题，提出基于能量模型的目标分类和度量学习方法. 利用样本特征空间中同类样本的低能量分布特性, 设计对比能量损失函数，形式上表达为训练样本在真实目标类别上的损失函数响应和非目标类别上的响应之差，可以更准确地增大目标响应，抑制非目标响应, 提高了分类准确率，使得同类样本特征更聚集、异类样本特征更远离. 在多个行人再识别和车辆再识别数据集上的测试结果显示, 相对于Soft-max和Triplet混合损失函数, 利用能量模型可以提升网络训练效率，提高目标再识别准确率.

Person and vehicle re-identification based on energy model

An energy-based object detection and metric learning method was proposed in order to solve the intensive computational cost and low accuracy issues in the training process of person re-identification and vehicle re-identification (re-ID) algorithms. A contrastive energy-based loss was designed based on the low energy characteristic of the same samples in the feature space, which took the form of the difference between samples’ true target response and non-target response of the training loss. The target response can be increased more accurately, and the non-target response can be suppressed. The classification accuracy can be improved, and the features within the same categories can stay more compact while different identities keep a distance away. Experiments on several person re-ID and vehicle re-ID databases showed that the efficiency of the training process was improved and the object re-ID accuracy was enhanced compared to the fused loss of Soft-max and Triplet.