  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.
