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

Detecting dangerous traffic agents in videos captured by a dashboard camera (dashcam) mounted on vehicles is essential to ensure safe navigation in complex driving environments. Crash-related videos are corner cases in driving-related big data, and pre-crash processes are transient and complex. Besides, risky and non-risky traffic agents can be similar in their appearance. These make the localization of risky traffic agents in driving videos particularly challenging. In addressing the challenges, 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 a crash. Fusing the two streams of global and object-level features, AM-Net predicts the riskiness scores of traffic agents in the video. This paper also introduces a new benchmark dataset called Risky Object Localization (ROL), which contains spatial, temporal, and categorical annotations of the crash, 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 contributions of its functional constituents.