Multi-class object detection by part based approach

This paper presents an efficient method to detect multiple objects in multiple views by part based approach in computer vision. The part based method is adapted to detect and classify the multiple parts of objects as car/person in order to overcome the occlusion. For detecting the multiple instances of object, the cascaded structure is considered, with each node as joint boosting classifier with shared features. Features extracted are Haar-rectangular features, as it efficiently captures the structural property of the object. With joint boosting algorithm, the features are shared among different classes, thus in turn reducing the computational complexity and detection time. The classifier efficiency is analysed by two parameters namely precision and recall. Although the proposed scheme is validated for car and pedestrian classes, the training and detection techniques used in this scheme can be generalized for any object class.