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Anticipation of accidents ahead of time in autonomous and non-autonomousvehicles aids in accident avoidance. In order to recognize abnormal events suchas traffic accidents in a video sequence, it is important that the networktakes into account interactions of objects in a given frame. We propose a novelFeature Aggregation (FA) block that refines each object's features by computinga weighted sum of the features of all objects in a frame. We use FA block alongwith Long Short Term Memory (LSTM) network to anticipate accidents in the videosequences. We report mean Average Precision (mAP) and Average Time-to-Accident(ATTA) on Street Accident (SA) dataset. Our proposed method achieves thehighest score for risk anticipation by predicting accidents 0.32 sec and 0.75sec earlier compared to the best results with Adaptive Loss and dynamicparameter prediction based methods respectively.

Global Feature Aggregation for Accident Anticipation