Research on Driverxe2x80x99s Distracted Behavior Detection Method Based on Multiclass Classification and SVM To reduce the occurrence of traffic accidents caused by distraction. a detection method based on histogram of oriented gradient (HOG) and support vector machine (SVM) is proposed for driverxe2x80x99s distraction behavior in this paper. Interest region of driver was detected first from video image, also the image was enhanced, denoised and normalized. Then the histogram of oriented gradient is used to extract the feature of the image. Meanwhile, the crossvalidation method is used to optimize parameters in SVM. Finally, the effectiveness of the method is verified by compared with classical SVM algorithm and Local Binary Pattern algorithm (LBP) based on SVM algorithms. The results show that, the proposed method can obtain better classification accuracy.

Localized Background Subtraction FeatureBased Approach for Vehicle Counting This paper presents vehicle counting using local features derived from background subtraction method combined with the traditional SVM classier. The main advantage of the proposed method is that it manages to overcome counting problem in different challenging situations such as during rainy day condition, which is yet a difficult problem in the standard background subtraction method. In this method, the background subtraction is performed within a small predefined region of a lane or road, thus increasing the computational speed. Next, two key features i.e. area covered by the motion indexed graph and number of edge pixel computed in the designated areas in the image are computed to form a 2D feature vector. Finally, we use SVM for classifying this vector into either vehicle or noise. Comparisons between the proposed method and other method show the potential of our approach.