论文标题
“谁在我周围开车？”使用深神经功能的独特车辆实例分类
"Who is Driving around Me?" Unique Vehicle Instance Classification using Deep Neural Features
论文作者
论文摘要
意识到其他流量是自动驾驶汽车在现实世界中运行的先决条件。在本文中，我们展示了如何使用对象检测CNN的固有特征图来唯一地识别仪表板馈送的车辆。预验证的“ Yolo”网络的特征地图用于创建700个深入集成特征签名（DIFS），从高分辨率数据集的35辆车的20个不同图像和340个签名中的20个不同图像的签名从20个较低分辨率跟踪基准数据集的20个不同图像的340个签名中创建。 Yolo网络经过培训以对一般对象类别进行分类，例如将检测到的物体分类为“汽车”或“卡车”。使用网络中部的特征图创建的DIFS，使用了5倍最近的邻居（1NN）分类，以识别高分辨率数据的速度为96.7 \％的唯一车辆，对于低分辨率数据，速率为86.8 \％。我们得出的结论是，通过创建深层集成的特征签名（DIFS），可以成功地使用训练有素的深层神经检测网络，以区分不同类别的不同类别。
Being aware of other traffic is a prerequisite for self-driving cars to operate in the real world. In this paper, we show how the intrinsic feature maps of an object detection CNN can be used to uniquely identify vehicles from a dash-cam feed. Feature maps of a pretrained `YOLO' network are used to create 700 deep integrated feature signatures (DIFS) from 20 different images of 35 vehicles from a high resolution dataset and 340 signatures from 20 different images of 17 vehicles of a lower resolution tracking benchmark dataset. The YOLO network was trained to classify general object categories, e.g. classify a detected object as a `car' or `truck'. 5-Fold nearest neighbor (1NN) classification was used on DIFS created from feature maps in the middle layers of the network to correctly identify unique vehicles at a rate of 96.7\% for the high resolution data and with a rate of 86.8\% for the lower resolution data. We conclude that a deep neural detection network trained to distinguish between different classes can be successfully used to identify different instances belonging to the same class, through the creation of deep integrated feature signatures (DIFS).
