An Attention-Guided Multistream Feature Fusion Network for Early Localization of Risky Traffic Agents in Driving Videos

Abstract

Detecting dangerous traffic agents in videos captured by vehicle-mounted dashboard cameras (dashcams) is essential to ensure safe navigation in complex environments. Accident-related videos are just a minor portion of the driving-related big data, and the transient pre-accident process is highly dynamic and complex. Besides, risky and non-risky traffic agents can be similar in their appearance. These make risky traffic agent localization in the driving video particularly challenging. To this end, this paper proposes an attention-guided multistream feature fusion network (AM-Net) to localize dangerous traffic agents from dashcam videos ahead of potential accidents. Two Gated Recurrent Unit (GRU) networks use object bounding box and optical flow features extracted from consecutive video frames to capture spatio-temporal cues for distinguishing risky traffic agents. An attention module, coupled with the GRUs, learns to identify traffic agents that are relevant to an accident. Fusing the two streams of global and object-level features, AM-Net predicts the riskiness scores of traffic agents in the video. In supporting this study, the paper also introduces a new benchmark dataset called Risky Object Localization (ROL). The dataset contains spatial, temporal, and categorical annotations of the accident, object, and scene-level attributes. The proposed AM-Net achieves a promising performance of 85.59% AUC on the ROL dataset. Additionally, the AM-Net outperforms the current state-of-the-art for video anomaly detection by 3.5% AUC on the public DoTA dataset. A thorough ablation study further reveals AM-Net's merits by assessing the impact of its constituents.