Surveillance-based Collision-time Analysis of Road-crossing Pedestrians

Moayed, Zahra; Chien, Hsiang-Jen; Zhang, Derek; Klette, Reinhard

doi:10.1109/itsc.2019.8917448

Cited by 3 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with frame-level deep Vision-TAD methods, object-centric approaches concentrate on object-level temporal consistency and follow the object detection and tracking stages to generate the trajectories, where various detectors (e.g., mask-RCNN [67], FasterRCNN [68], YOLOv4 [69], YOLOv5 [70], etc.) and many object association approaches (SORT [71], DeepSort [72], Kalman Filter [69], [73], Hungarian algorithm [69], etc.) are utilized.…”

Section: Object-centric Deep Vision-tad Methodsmentioning

confidence: 99%

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

Fang

Qiao²,

Bai³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

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.

show abstract