Performance of optical flow techniques
International Journal of Computer Vision
Machine Learning
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Fields of Experts: A Framework for Learning Image Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
On the Spatial Statistics of Optical Flow
International Journal of Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Traffic sign recognition system with β -correction
Machine Vision and Applications
Pedestrian Protection Systems: Issues, Survey, and Challenges
IEEE Transactions on Intelligent Transportation Systems
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We address the problem of event classification for intelligent vehicle navigation system from video sequences acquired by a front mounted camera in complex urban scenes. Although in normal driving condition, large variety of events could be found and be preferably attached to an alerting system in a vehicle, there have been relatively narrow research activities on driving scene analysis, for example, finding local information such as lanes, pedestrians, traffic signs or light detections. Yet, the above-mentioned methods only provide limited performance due to many challenges in normal urban driving conditions, i.e. complex background, inhomogeneous illumination, occlusion, etc. In this paper, we tackle the problem of classification of various events by learning regional optical flows to detect some important events (very frequent occurring and involving riskiness on driving) using low cost front mounted camera equipment. We approached the problem as follows: First, we present an optical flow-based event detection method by regional significance analysis with the introduction of a novel significance map based on regional histograms of flow vectors; Second, we present a global and a local method to robustly detect ego-motion-based events and target-motion-based events. In our experiments, we achieved classification accuracy about 91% on average tested with two classifiers (Bayesian and SVM). We also show the performance of the method in terms of computational complexity achieving about 14.3 fps on a laptop computer with Intel Pentium 1.2 Ghz.