What can we learn from accident videos?

A recent rise of windshield mounted cameras in cars has given access to large amount of videos from real world traffic accidents. This paper aims to analyze what can be learned from that video data using current state-of-the-art computer vision techniques. We also discuss what is currently missing to fully understand these videos. A substantial number of accident sequences has been acquired and manually evaluated for this analysis. The ability to fully automatically analyze large numbers of traffic accidents from videos will allow to identify potentially dangerous traffic scenarios, improve driving safety and driver warning systems.

[1]  ZuWhan Kim,et al.  Robust Lane Detection and Tracking in Challenging Scenarios , 2008, IEEE Transactions on Intelligent Transportation Systems.

[2]  Luhong Liang,et al.  Fast car detection using image strip features , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Flávio Cunto.  Assessing Safety Performance of Transportation Systems using Microscopic Simulation , 2008 .

[4]  Wei Liu,et al.  Vision-Based Real-Time Lane Marking Detection and Tracking , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[5]  Sergio A. Velastin,et al.  A Review of Computer Vision Techniques for the Analysis of Urban Traffic , 2011, IEEE Transactions on Intelligent Transportation Systems.

[6]  Deva Ramanan,et al.  Analyzing 3D Objects in Cluttered Images , 2012, NIPS.

[7]  Tom Drummond,et al.  Scalable Monocular SLAM , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[8]  Danping Zou,et al.  CoSLAM: Collaborative Visual SLAM in Dynamic Environments , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  K. Madhava Krishna,et al.  Realtime multibody visual SLAM with a smoothly moving monocular camera , 2011, 2011 International Conference on Computer Vision.

[10]  Stefan Roth,et al.  People-tracking-by-detection and people-detection-by-tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Richard Szeliski,et al.  Pushing the Envelope of Modern Methods for Bundle Adjustment , 2012, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Tarek Sayed,et al.  Large-Scale Automated Analysis of Vehicle Interactions and Collisions , 2010 .

[13]  H. Opower.  Multiple view geometry in computer vision , 2002 .