Global and local training for moving object classification in surveillance-oriented scene
This paper presents a new training framework for multi-class moving object classification in surveillance-oriented scene. In many practical multi-class classification tasks, the instances are close to each other in the input feature space when they have similar features. These instances may have different class labels. Since the moving objects may have various view and shape, the above phenomenon is common in multi-class moving object classification. In our framework, firstly the input feature space is divided into several local clusters. Then, global training and local training are carried out sequential with an efficient online learning based algorithm. The induced global classifier is used to assign candidate instances to the most reliable clusters. Meanwhile, the trained local classifiers within those clusters can determine which classes the candidate instances belong to. Our experimental results illustrate the effectiveness of our method for moving object classification in surveillance-oriented scene.