A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Model-Based Vehicle Segmentation Method for Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Tracking and Segmentation of Highway Vehicles in Cluttered and Crowded Scenes
WACV '08 Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision
View independent recognition of human-vehicle interactions using 3-D models
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
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This paper presents a novel approach to vehicle detection in highway surveillance videos. This method incorporates well-studied computer vision and machine learning techniques to form an unsupervised system, where vehicles are automatically “learned” from video sequences. First an enhanced adaptive background mixture model is used to identify positive and negative examples. Then a classifier is trained with these examples. In the detection phase, both background subtraction and the classifier are used to achieve very accurate results while not compromising efficiency. We tested our method with very low-, medium- and high-quality, crowded and very crowded surveillance videos and got detection accuracies ranging between 90% to 96%.