论文标题
VOC-REID：基于车辆导向相机的车辆重新识别
VOC-ReID: Vehicle Re-identification based on Vehicle-Orientation-Camera
论文作者
论文摘要
由于较高的级别差异和较小的级别差异，车辆重新识别是一项具有挑战性的任务。在这项工作中，我们专注于由类似背景和形状引起的故障案例。他们对相似性构成偏见，从而更容易忽略细粒度的信息。为了减少偏见，我们提出了一种名为VOC-REID的方法，以整体和改革的背景/形状相似性，将三胞胎式车辆导向相机与摄像机/方向重新识别相似。首先，我们分别训练车辆，方向和相机重新识别的模型。然后，我们将方向和相机相似性作为惩罚来获得最终相似之处。此外，我们提出了一个高性能基线，以袋装和弱监督的数据增强为增强。我们的算法在2020年NVIDIA AI CITY CAMPLIENT在车辆重新识别中获得了第二名。
Vehicle re-identification is a challenging task due to high intra-class variances and small inter-class variances. In this work, we focus on the failure cases caused by similar background and shape. They pose serve bias on similarity, making it easier to neglect fine-grained information. To reduce the bias, we propose an approach named VOC-ReID, taking the triplet vehicle-orientation-camera as a whole and reforming background/shape similarity as camera/orientation re-identification. At first, we train models for vehicle, orientation and camera re-identification respectively. Then we use orientation and camera similarity as penalty to get final similarity. Besides, we propose a high performance baseline boosted by bag of tricks and weakly supervised data augmentation. Our algorithm achieves the second place in vehicle re-identification at the NVIDIA AI City Challenge 2020.
