Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
On Traffic Density Estimation with a Boosted SVM Classifier
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
IEEE Transactions on Circuits and Systems for Video Technology
A bayesian network approach to traffic flow forecasting
IEEE Transactions on Intelligent Transportation Systems
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Information on the vehicular traffic density in an intelligent transport system (ITS) is presently obtained mainly through loop detectors (LD), traffic radars and surveillance cameras. However, the difficulties and cost of installing loop detectors and traffic radars tend to be significant. Currently, a more advanced method of circumventing this is to develop a sort of virtual loop detector (VLD) by using video content understanding technology to simulate behavior of a loop detector and to further estimate the traffic flow from a surveillance camera. Such a virtual loop detector that requires supervised training with human intervention for its setup. Difficulties also arise when attempting to obtain a reliable and real-time VLD under different illumination, weather conditions and static shadows. In this paper, we study the effectiveness of texture features in describing the traffic density, and propose a real-time VLD based on on-line SVM classifier and a background modeling technique (OSVM-BG) to estimate the traffic density information probabilistically and automatically. The system uses feedback from background modeling to train and update its SVM kernel to self-adapt to various lighting environments. Experimental results show that the system outperforms an existing algorithm and achieves an average accuracy of 89.43% under various illumination changes, weather conditions and especially changing static shadows in daytime.