[Paper Review] Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization

Kuo-Hao Zeng, Shih-Han Chou+3 more|arXiv (Cornell University)|May 18, 2017

Anomaly Detection Techniques and Applications31 references3 citations

TL;DR

This paper proposes a novel agent-centric risk assessment framework that combines dynamic parameter prediction with a soft-attention RNN to anticipate accidents and localize risky regions in videos. By modeling spatial and appearance-wise interactions between agents and environments, and using RNN-based trajectory imagination for long-term risk forecasting, the method achieves state-of-the-art performance on both accident anticipation and risky region localization tasks, outperforming strong baselines on the newly introduced Epic Fail and Street Accident datasets.

ABSTRACT

For survival, a living agent must have the ability to assess risk (1) by temporally anticipating accidents before they occur, and (2) by spatially localizing risky regions 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 soft-attention 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.

Motivation & Objective

• To develop a computer vision framework that enables agents to anticipate accidents before they occur by analyzing past and current behavior.
• To localize risky regions in the environment where accidents are likely to happen, based on agent-environment interactions.
• To model complex spatiotemporal dependencies and causal relationships underlying risk events through deep learning.
• To introduce a new benchmark dataset, Epic Fail, for agent-centric risk assessment in visual video understanding.
• To enable long-term risk forecasting by generating and evaluating imagined future trajectories of agents.

Proposed method

• The method employs a soft-attention RNN to encode agent behavior into distributed representations over time.
• A dynamic parameter prediction layer is introduced to model relative spatial relations and coupled appearances between the agent and candidate risky regions.
• The model uses a second RNN to process agent representations and predict accident anticipation probabilities over time.
• Future trajectories are imagined using the generative capability of the RNN, which are then fed back into the model for long-term risk assessment.
• The system leverages Faster R-CNN to propose candidate risky regions, which are then scored using the dynamic parameter layer.
• The framework is trained end-to-end with joint optimization for both accident anticipation and risky region localization.

Experimental results

Research questions

• RQ1 Can a deep learning model effectively anticipate accidents before they occur by analyzing agent behavior in video?
• RQ2 Can the model accurately localize spatial regions in a scene that are most likely to be involved in future accidents?
• RQ3 How can relative spatial and appearance-wise interactions between agents and environmental regions be modeled to improve risk assessment?
• RQ4 To what extent can imagined future trajectories enhance long-term risk prediction performance?
• RQ5 How does the proposed dynamic parameter prediction mechanism compare to hard-attention or pooling-based alternatives in risk modeling?

Key findings

• The proposed method, L-RAI, achieves 15.1% mAP on the Epic Fail dataset and 45.4% mAP on the Street Accident dataset for accident anticipation, outperforming all baselines.
• The addition of memory and trajectory imagination significantly improves both mAP and ATTA metrics, demonstrating the benefit of long-term temporal modeling.
• L-RAI outperforms L-R*CNN by 1.1% mAP on EF and 1.8% on SA, despite L-R*CNN being a strong baseline with hard-attention.
• The dynamic parameter prediction layer outperforms social pooling (SP) and dynamic spatial attention (DSA), indicating its effectiveness in modeling agent-region interactions.
• The model shows strong generalization, correctly identifying diverse risky regions such as cars and bars, and reasonably flagging inflatable pools as potential risks in failure cases.
• Oracle performance reaches 75.7% mAP on EF and 92.8% on SA, indicating that the current model is still far from the upper bound, leaving room for improvement.