The goal of fine-grained vehicle recognition is to identify the exact subtype of the vehicle from a given image. It plays an important role in the intelligent traffic surveillance system. Although fine-grained vehicle recognition has attracted more and more research interest, it remains as an open problem for vehicle images taken from arbitrary viewpoints. In this study, we present a one-stage deep multi-task learning framework for fine-grained vehicle recognition in traffic surveillance, which performs the fine-grained vehicle recognition and viewpoint estimation simultaneously. In the proposed framework, the fine-grained vehicle recognition is the main task which classifies images into different types, and the viewpoint estimation task is the auxiliary task to learn helpful viewpoint-aware features for improving the main task. We evaluate our method on two common used large-scale fine-grained vehicle recognition datasets, including BoxCars116k and CompCars. The experimental results testify that the proposed multi-task framework can improve the accuracy by incorporating the viewpoint information of the vehicles. In comparison to the state-of-the-art, our approach goes beyond, only requiring 3D bounding box for the training phase, which is important for future inferences using the trained model.

中文翻译：

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用于有效分析基底集成波导结构的多重散射方法


细粒度车辆识别的目标是从给定图像中识别车辆的确切子类型。它在智能交通监控系统中发挥着重要作用。尽管细粒度车辆识别吸引了越来越多的研究兴趣，但对于从任意视角拍摄的车辆图像来说，它仍然是一个悬而未决的问题。在本研究中，我们提出了一种用于交通监控中细粒度车辆识别的单阶段深度多任务学习框架，该框架同时执行细粒度车辆识别和视点估计。在所提出的框架中，细粒度车辆识别是将图像分类为不同类型的主要任务，视点估计任务是学习有用的视点感知特征以改进主要任务的辅助任务。我们在两个常用的大规模细粒度车辆识别数据集（包括 BoxCars116k 和 CompCars）上评估我们的方法。实验结果证明，所提出的多任务框架可以通过结合车辆的视点信息来提高准确性。与最先进的方法相比，我们的方法更进一步，在训练阶段只需要 3D 边界框，这对于未来使用训练模型进行推理非常重要。