  We focus on the problem of detecting traffic events in a surveillance
scenario, including the detection of both vehicle actions and traffic
collisions. Existing event detection systems are mostly learning-based and have
achieved convincing performance when a large amount of training data is
available. However, in real-world scenarios, collecting sufficient labeled
training data is expensive and sometimes impossible (e.g. for traffic collision
detection). Moreover, the conventional 2D representation of surveillance views
is easily affected by occlusions and different camera views in nature. To deal
with the aforementioned problems, in this paper, we propose a training-free
monocular 3D event detection system for traffic surveillance. Our system
firstly projects the vehicles into the 3D Euclidean space and estimates their
kinematic states. Then we develop multiple simple yet effective ways to
identify the events based on the kinematic patterns, which need no further
training. Consequently, our system is robust to the occlusions and the
viewpoint changes. Exclusive experiments report the superior result of our
method on large-scale real-world surveillance datasets, which validates the
effectiveness of our proposed system.
