DSAM: A Distance Shrinking with Angular Marginalizing Loss for High Performance Vehicle Re-identificatio

Vehicle Re-identification (ReID) is an important yet challenging problem in computer vision. Compared to other visual objects like faces and persons, vehicles simultaneously exhibit much larger intraclass viewpoint variations and interclass visual similarities, making most exiting loss functions designed for face recognition and person ReID unsuitable for vehicle ReID.To obtain a high-performance vehicle ReID model, we present a novel Distance Shrinking with Angular Marginalizing (DSAM) loss function to perform hybrid learning in both the Original Feature Space (OFS) and the Feature Angular Space (FAS) using the local verification and the global identification information. Specifically, it shrinks the distance between samples of the same class locally in the Original Feature Space while keeps samples of different classes far away in the Feature Angular Space. The shrinking and marginalizing operations are performed during each iteration of the training process and are suitable for different SoftMax based loss functions. We evaluate the DSAM loss function on three large vehicle ReID datasets with detailed analyses and extensive comparisons with many competing vehicle ReID methods. Experimental results show that our DSAM loss enhances the SoftMax loss by a large margin on the PKU-VD1-Large dataset: 10.41% for mAP, 5.29% for cmc1, and 4.60% for cmc5. Moreover, the mAP is increased by 9.34% on the PKU-VehicleID dataset and 8.73% on the VeRi-776 dataset. Source code will be released to facilitate further studies in this research direction.

DSAM：用于高性能车辆重新识别的角边际损耗缩小的距离

车辆重新识别（ReID）是计算机视觉中的一个重要但具有挑战性的问题。与其他视觉对象（如人脸和人）相比，车辆同时表现出更大的类内视点变化和类间视觉相似性，这使得大多数用于面部识别和人ReID的现有损失函数不适合车辆ReID。.. 为了获得高性能的车辆ReID模型，我们提出了一种新颖的具有角边距缩小的距离收缩（DSAM）损失函数，可以使用本地验证和原始特征空间（OFS）和特征角空间（FAS）进行混合学习。全局标识信息。具体来说，它会在原始要素空间中局部缩小同一类别的样本之间的距离，而在要素角空间中使不同类别的样本保持较远的距离。收缩和边缘化操作在训练过程的每次迭代期间执行，并且适用于不同的基于SoftMax的损失函数。我们对三个大型车辆ReID数据集评估了DSAM损失函数，并进行了详细分析并与许多竞争性车辆ReID方法进行了广泛比较。实验结果表明，我们的DSAM损失在PKU-VD1-Large数据集上大大提高了SoftMax损失：mAP为10.41％，cmc1为5.29％，cmc5为4.60％。此外，mKU在PKU-VehicleID数据集上增加了9.34％，在VeRi-776数据集上增加了8.73％。将发布源代码以促进在此研究方向上的进一步研究。 （阅读更多）