论文标题
使用边界椭圆的非锚基车辆检测进行交通监视
Non-anchor-based vehicle detection for traffic surveillance using bounding ellipses
论文作者
论文摘要
用于交通监视的摄像头通常是钢管杆安装的，并产生反映鸟类视图的图像。通常，在此类图像中的车辆通常采用椭圆形。当车辆方向与盒子边缘不平行时，车辆的边界盒通常包括一个大空白。为了解决这个问题，本研究将边界椭圆应用于基于非锚的单杆检测模型（Centernet）。由于此模型不取决于锚点，因此需要在预测边界框之间计算联合（IOU）的相交的非最大抑制（NMS）是不需要推断的。还通过添加分段头扩展了Centernet模型的点网，还通过边界椭圆进行了测试。选择了另外两个基于锚的单杆检测模型（YOLO4和SSD）作为比较参考。根据一个本地数据集进行了比较模型性能，该数据集用边界框和椭圆形进行了双重注释。结果，具有边界椭圆的两个模型的性能超过了带有边界框的参考模型的性能。当在开放数据集（UA-DETRAC）上鉴定了椭圆模型的骨架时，性能进一步提高。为YOLO4开发的数据增强方案也提高了所提出的模型的性能。结果，具有边界椭圆的百分点的最佳地图得分超过0.9。
Cameras for traffic surveillance are usually pole-mounted and produce images that reflect a birds-eye view. Vehicles in such images, in general, assume an ellipse form. A bounding box for the vehicles usually includes a large empty space when the vehicle orientation is not parallel to the edges of the box. To circumvent this problem, the present study applied bounding ellipses to a non-anchor-based, single-shot detection model (CenterNet). Since this model does not depend on anchor boxes, non-max suppression (NMS) that requires computing the intersection over union (IOU) between predicted bounding boxes is unnecessary for inference. The SpotNet that extends the CenterNet model by adding a segmentation head was also tested with bounding ellipses. Two other anchor-based, single-shot detection models (YOLO4 and SSD) were chosen as references for comparison. The model performance was compared based on a local dataset that was doubly annotated with bounding boxes and ellipses. As a result, the performance of the two models with bounding ellipses exceeded that of the reference models with bounding boxes. When the backbone of the ellipse models was pretrained on an open dataset (UA-DETRAC), the performance was further enhanced. The data augmentation schemes developed for YOLO4 also improved the performance of the proposed models. As a result, the best mAP score of a CenterNet with bounding ellipses exceeds 0.9.
