  Detecting dangerous traffic agents in videos captured by vehicle-mounted
dashboard cameras (dashcams) is essential to facilitate safe navigation in a
complex environment. Accident-related videos are just a minor portion of the
driving video big data, and the transient pre-accident processes are highly
dynamic and complex. Besides, risky and non-risky traffic agents can be similar
in their appearance. These make risky object 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. 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 dangerous traffic agents. An
attention module coupled with the GRUs learns to attend to the traffic agents
relevant to an accident. Fusing the two streams of features, AM-Net predicts
the riskiness scores of traffic agents in the video. In supporting this study,
the paper also introduces a benchmark dataset called Risky Object Localization
(ROL). The dataset contains spatial, temporal, and categorical annotations with
the accident, object, and scene-level attributes. The proposed AM-Net achieves
a promising performance of 85.73% AUC on the ROL dataset. Meanwhile, the AM-Net
outperforms current state-of-the-art for video anomaly detection by 6.3% AUC on
the DoTA dataset. A thorough ablation study further reveals AM-Net's merits by
evaluating the contributions of its different components.
