Agent-Centric Risk Assessment:
Accident Anticipation and Risky Region Localization
Abstract
For survival, a living agent (e.g., human in Fig. 1(a))
must have the ability to assess risk (1) by temporally anticipating accidents before they occur (Fig. 1(b)), and (2) by
spatially localizing risky regions (Fig. 1(c)) in the environment to move away from threats. In this paper, we take an
agent-centric approach to study the accident anticipation
and risky region localization tasks. We propose a novel softattention Recurrent Neural Network (RNN) which explicitly
models both spatial and appearance-wise non-linear interaction between the agent triggering the event and another
agent or static-region involved. In order to test our proposed method, we introduce the Epic Fail (EF) dataset consisting of 3000 viral videos capturing various accidents.
In the experiments, we evaluate the risk assessment accuracy both in the temporal domain (accident anticipation)
and spatial domain (risky region localization) on our EF
dataset and the Street Accident (SA) dataset. Our method
consistently outperforms other baselines on both datasets