Robust Vehicle Detection for Tracking in Highway Surveillance Videos using Unsupervised Learning

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%.