Domain Adaptation for Car Accident Detection in Videos
Source of Publication
2019 9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019
© 2019 IEEE. In this paper, we implement a deep learning model for car accident detection using synthetic videos while adapting the model, using domain adaptation (DA), to real videos from CCTV traffic cameras. The synthetic data are rendered using a video game. The reason to use such data is the lack of real videos of car crashes from CCTV. Though a video game may allow us to generate car crashes in a variety of scenarios, the distinction in synthetic and real videos can negatively affect the model's performance. Accordingly, our aim is three-fold: render numerous synthetic videos having significant variations, train a 3D CNN based deep model on the collected videos, and use DA to adapt the model from synthetic to real videos. Our experimental results, obtained under a variety of experimental setups, demonstrate the feasibility of using our approach for car accident detection in real videos.
Institute of Electrical and Electronics Engineers Inc.
Accident recognition, Computer Vision, Deep Learning, Domain Adaptation, Machine Learning
Batanina, Elizaveta; Bekkouch, Imad Eddine Ibrahim; Youssry, Youssef; Khan, Adil; Khattak, Asad Masood; and Bortnikov, Mikhail, "Domain Adaptation for Car Accident Detection in Videos" (2019). All Works. 1323.
Indexed in Scopus