Corner based statistical modelling in vehicle detection under various condition for traffic surveillance

The paper proposes a robust approach to detect and track the vehicle under various climatic conditions and in the presence of camera shake, shadows, sudden illumination change. Corners have significant features to detect and track the vehicle. Corner points from the vehicular region are segmented from non - vehicular regions based on the statistical background corner point model. The foreground corner points that belong to the vehicular region are grouped using Euclidean distance as they are closely associated with each other. The flickering effects caused by the corner detection algorithm are handled by tracking these corner points. The detection accuracy of the algorithm is 94.32%.

     

Geometric invariant features for the detection and analysis of vehicle

The intelligent traffic management system (ITS) is one of the active research areas. Vehicle detection is a major role in traffic analysis. In the paper, analysis of detecting vehicles is proposed based on the features posed by the vehicle. The foreground pixels from image are extracted by histogram based foreground segmentation. After segmenting, Hu-Moments and Eigen values features are extracted and normalized. The classifiers are trained with the extracted Hu-Moments and Eigen values. The experiments are conducted on different benchmark datasets, and results are analysed considering the overall classification accuracy. Results of the algorithm are satisfactory and acceptable in real time.