  In the field of transportation, it is of paramount importance to address and
mitigate illegal actions committed by both motor and non-motor vehicles. Among
those actions, wrong-way cycling (i.e., riding a bicycle or e-bike in the
opposite direction of the designated traffic flow) poses significant risks to
both cyclists and other road users. To this end, this paper formulates a
problem of detecting wrong-way cycling ratios in CCTV videos. Specifically, we
propose a sparse sampling method called WWC-Predictor to efficiently solve this
problem, addressing the inefficiencies of direct tracking methods. Our approach
leverages both detection-based information, which utilizes the information from
bounding boxes, and orientation-based information, which provides insights into
the image itself, to enhance instantaneous information capture capability. On
our proposed benchmark dataset consisting of 35 minutes of video sequences and
minute-level annotation, our method achieves an average error rate of a mere
1.475% while taking only 19.12% GPU time of straightforward tracking methods
under the same detection model. This remarkable performance demonstrates the
effectiveness of our approach in identifying and predicting instances of
wrong-way cycling.
