论文标题
基于互补功能的车辆重新识别
Vehicle Re-Identification Based on Complementary Features
论文作者
论文摘要
在这项工作中，我们向AI City Challenge 2020（AIC2020）的车辆重新识别（车辆重新ID）轨道介绍了解决方案。车辆重新ID的目的是检索在多个摄像头上出现的同一辆车辆，并且可以为智能交通系统（ITS）和智能城市做出巨大贡献。由于车辆的方向，照明和类间的相似性，很难获得强大而歧视性的表示。对于AIC2020中的车辆重新ID轨道，我们的方法是融合从不同网络中提取的功能，以利用这些网络的优势并获得互补的功能。对于每个模型，使用多种方法，例如多损失，滤镜嫁接，半监督，用于提高表示能力尽可能更好。城市尺度多摄像机车辆重新识别的最佳性能证明了我们的方法的优势，我们在AIC2020的车辆重新ID赛道中排名第五。这些代码可在https://github.com/gggy/aic2020_reid上找到。
In this work, we present our solution to the vehicle re-identification (vehicle Re-ID) track in AI City Challenge 2020 (AIC2020). The purpose of vehicle Re-ID is to retrieve the same vehicle appeared across multiple cameras, and it could make a great contribution to the Intelligent Traffic System(ITS) and smart city. Due to the vehicle's orientation, lighting and inter-class similarity, it is difficult to achieve robust and discriminative representation feature. For the vehicle Re-ID track in AIC2020, our method is to fuse features extracted from different networks in order to take advantages of these networks and achieve complementary features. For each single model, several methods such as multi-loss, filter grafting, semi-supervised are used to increase the representation ability as better as possible. Top performance in City-Scale Multi-Camera Vehicle Re-Identification demonstrated the advantage of our methods, and we got 5-th place in the vehicle Re-ID track of AIC2020. The codes are available at https://github.com/gggcy/AIC2020_ReID.
