A deep learning framework for detecting and localizing abnormal pedestrian behaviors at grade crossings

Author:

Jiang Zhuocheng,Song Ge,Qian Yu,Wang YiORCID

Funder

Federal Railroad Administration

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference41 articles.

1. FRA (2019) Highway-rail crossing handbook - Third Edition. https://safety.fhwa.dot.gov/hsip/xings/com_roaduser/fhwasa18040/fhwasa18040v2.pdf

2. FRA (2018) National strategy to prevent trespassing on railroad property. https://www.fra.dot.gov/eLib/Details/L19817

3. FRA (2021) Highway/rail grade crossing incidents. https://railroads.dot.gov/accident-and-incident-reporting/highwayrail-grade-crossing-incidents/highwayrail-grade-crossing

4. Pang G, Shen C, Cao L, Hengel A (2020) Deep learning for anomaly detection: a review. ACM Comput Surv Mar 54(2):1–38

5. Zhou JT, Du J, Zhu H, Peng X, Liu Y, Goh R (2019) Anomalynet: an anomaly detection network for video surveillance. IEEE Trans Inf Forensics Secur Oct 14(10):2537–2550

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