Moving object recognition based on SVM and binary decision tree

Abstract

This paper designs an algorithm for the moving object recognition based on support vector machine (SVM) in order to identify and classify the moving objects accurately. In view of the advantages of support vector machine in small sample, nonlinear, and high dimensional pattern recognition, a classifier is constructed based on support vector machine (SVM) is constructed. A feature vector is presented to train and classify support vector machines, which is composed of shape features and used to classify the samples. Furthermore, the support vector machine and binary decision tree are combined to form the multi class classifier. The object feature vector is used as the input of SVM, and the classifier is used to classify the detected moving objects. Finally, the experimental results show that the proposed algorithm can identify and classify different objects in video images accurately.