  Panoramic cycling videos can record 360{\deg} views around the cyclists.
Thus, it is essential to conduct automatic road user analysis on them using
computer vision models to provide data for studies on cycling safety. However,
the features of panoramic data such as severe distortions, large number of
small objects and boundary continuity have brought great challenges to the
existing CV models, including poor performance and evaluation methods that are
no longer applicable. In addition, due to the lack of data with annotations, it
is not easy to re-train the models.
  In response to these problems, the project proposed and implemented a
three-step methodology: (1) improve the prediction performance of the
pre-trained object detection models on panoramic data by projecting the
original image into 4 perspective sub-images; (2) introduce supports for
boundary continuity and category information into DeepSORT, a commonly used
multiple object tracking model, and set an improved detection model as its
detector; (3) using the tracking results, develop an application for detecting
the overtaking behaviour of the surrounding vehicles.
  Evaluated on the panoramic cycling dataset built by the project, the proposed
methodology improves the average precision of YOLO v5m6 and Faster RCNN-FPN
under any input resolution setting. In addition, it raises MOTA and IDF1 of
DeepSORT by 7.6\% and 9.7\% respectively. When detecting the overtakes in the
test videos, it achieves the F-score of 0.88.
  The code is available on GitHub at github.com/cuppp1998/360_object_tracking
to ensure the reproducibility and further improvements of results.
