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.

中文翻译：

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COLLIDE-PRED：根据监控视频预测道路碰撞

预测道路异常（例如交通事故或交通违章）是交通监控中一项具有挑战性的任务。如果可以提前进行这样的预测，则可以控制许多损害。在这里，我们试图为具有计算机视觉和深层网络的交通监控视频制定自动碰撞预测的解决方案。它涉及对象检测，跟踪，轨迹估计和碰撞预测。我们提出了一种名为COLLIDE-PRED的端到端碰撞预测系统，该系统可以智能地集成运动对象的过去和将来轨迹的信息，以预测视频中的碰撞。它是一个从对象检测开始的管道，该管道用于对象跟踪，然后执行轨迹预测，最后以碰撞检测结束。可能发生碰撞的地方 以及可能导致碰撞的物体，都可以使用COLLIDE-PRED正确识别。所提出的方法已通过大量不同的视频进行了实验验证，并被证明可有效地提前识别事故。