  Predicting on-road abnormalities such as road accidents or traffic violations
is a challenging task in traffic surveillance. If such predictions can be done
in advance, many damages can be controlled. Here in our wok, we tried to
formulate a solution for automated collision prediction in traffic surveillance
videos with computer vision and deep networks. It involves object detection,
tracking, trajectory estimation, and collision prediction. We propose an
end-to-end collision prediction system, named as COLLIDE-PRED, that
intelligently integrates the information of past and future trajectories of
moving objects to predict collisions in videos. It is a pipeline that starts
with object detection, which is used for object tracking, and then trajectory
prediction is performed which concludes by collision detection. The probable
place of collision, and the objects those may cause the collision, both can be
identified correctly with COLLIDE-PRED. The proposed method is experimentally
validated with a number of different videos and proves to be effective in
identifying accident in advance.
