DRAMA: Joint Risk Localization and Captioning in Driving

Malla, Srikanth; Choi, Chiho; Dwivedi, Isht; Choi, Joon Hee; Li, Jiachen

doi:10.1109/wacv56688.2023.00110

Cited by 12 publications

(1 citation statement)

References 36 publications

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“…Recently, the work proposes a new dataset DRAMA [115] for joint risk localization and captioning in driving scenes. Different from the aforementioned works, they contribute insight into text caption prediction for important object localization.…”

Section: Driver Attention Map In An Crossing Accidentmentioning

confidence: 99%

Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios

Fang

Qiao²,

Bai³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Traffic accident detection and anticipation is an obstinate road safety problem and painstaking efforts have been devoted. With the rapid growth of video data, Vision-based Traffic Accident Detection and Anticipation (named Vision-TAD and Vision-TAA) become the last one-mile problem for safe driving and surveillance safety. However, the long-tailed, unbalanced, highly dynamic, complex, and uncertain properties of traffic accidents form the Out-of-Distribution (OOD) feature for Vision-TAD and Vision-TAA. Current AI development may focus on these OOD but important problems. What has been done for Vision-TAD and Vision-TAA? What direction we should focus on in the future for this problem? A comprehensive survey is important. We present the first survey on Vision-TAD in the deep learning era and the first-ever survey for Vision-TAA. The pros and cons of each research prototype are discussed in detail during the investigation. In addition, we also provide a critical review of 31 publicly available benchmarks and related evaluation metrics. Through this survey, we want to spawn new insights and open possible trends for Vision-TAD and Vision-TAA tasks.

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Section: Driver Attention Map In An Crossing Accidentmentioning

confidence: 99%