On Semantic Object Detection with Salient Feature
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Framework for Illumination Invariant Vehicular Traffic Density Estimation
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Using adaptive background subtraction into a multi-level model for traffic surveillance
Integrated Computer-Aided Engineering
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This paper proposes a novel approach of combining an unsupervised clustering scheme called AutoClass with Hidden Markov Models (HMMs) to determine the traffic density state in a Region Of Interest (ROI) of a road in a traffic video. Firstly, low-level features are extracted from the ROI of each frame. Secondly, an unsupervised clustering algorithm called AutoClass is then applied to the low-level features to obtain a set of clusters for each pre-defined traffic density state. Finally, four HMM models are constructed for each traffic state respectively with each cluster corresponding to a state in the HMM and the structure of HMM is determined based on the cluster information. This approach improves over previous approaches that used Gaussian Mixture HMMs (GMHMM) by circumventing the need to make an arbitrary choice on the structure of the HMM as well as determining the number of mixtures used for each density traffic state. The results show that this approach can classify the traffic density in a ROI of a traffic video accurately with the property of being able to handle the varying illumination elegantly.