  In traffic management, it is a very important issue to shorten the response
time by detecting the incidents (accident, vehicle breakdown, an object falling
on the road, etc.) and informing the corresponding personnel. In this study, an
anomaly detection framework for road junctions is proposed. The final judgment
is based on the trajectories followed by the vehicles. Trajectory information
is provided by vehicle detection and tracking algorithms on visual data
streamed from a fisheye camera. Deep learning algorithms are used for vehicle
detection, and Kalman Filter is used for tracking. To observe the trajectories
more accurately, the detected vehicle coordinates are transferred to the bird's
eye view coordinates using the lens distortion model prediction algorithm. The
system determines whether there is an abnormality in trajectories by comparing
historical trajectory data and instantaneous incoming data. The proposed system
has achieved 84.6% success in vehicle detection and 96.8% success in
abnormality detection on synthetic data. The system also works with a 97.3%
success rate in detecting abnormalities on real data.
