Fisheye cameras are widely employed in automatic parking, and the video stream object detection (VSOD) of the fisheye camera is a fundamental perception function to ensure the safe operation of vehicles. In past research work, the difference between the output of the deep learning model and the actual situation at the current moment due to the existence of delay of the perception system is generally ignored. But the environment will inevitably change within the delay time which may cause a potential safety hazard. In this paper, we propose a real-time detection framework equipped with a dual-flow perception module (dynamic and static flows) that can predict the future and alleviate the time-lag problem. Meanwhile, we use a new scheme to evaluate latency and accuracy. The standard bounding box is unsuitable for the object in fisheye camera images due to the strong radial distortion of the fisheye camera and the primary detection objects of parking perception are vehicles and pedestrians, so we adopt the rotate bounding box and propose a new periodic angle loss function to regress the angle of the box, which is the simple and accurate representation method of objects. The instance segmentation ground truth is used to supervise the training. Experiments demonstrate the effectiveness of our approach. Code is released at: https://gitee.com/hiyanyx/fisheye-streaming-perception.

翻译：鱼眼摄像头广泛应用于自动泊车，而鱼眼摄像头的视频流目标检测是保障车辆安全运行的基础感知功能。以往的研究工作中，通常忽略了因感知系统延迟导致的深度学习模型输出结果与当前时刻实际情况之间的差异。但延迟期间环境不可避免地发生变化，可能造成安全隐患。本文提出一种搭载双流感知模块（动态流与静态流）的实时检测框架，能够预测未来状态并缓解时滞问题。同时，我们采用新方案评估延迟与精度。由于鱼眼摄像头存在强径向畸变，且泊车感知的主要检测对象为车辆与行人，标准边界框不再适用。因此，我们采用旋转边界框，并提出一种新型周期性角度损失函数来回归边界框角度，该方法对物体表征简单且准确。训练过程中使用实例分割真值进行监督。实验证明了我们方法的有效性。代码已开源至：https://gitee.com/hiyanyx/fisheye-streaming-perception。