Moment invariants based object recognition for different pose and appearances in real scenes

Object recognition in real scenes is a central problem in computer vision. In this paper we propose a new approach for shape based recognition of objects in real scenes. This approach uses moment invariants for identification of shape features. Moment Invariants are functions of central moments. They are invariant against linear transformations such as rotation, translation and scaling. Therefore, their integration provides recognition of objects in real scenes with different pose and appearances. In this way, the proposed approach does not only provide invariant object recognition, but also capable of dealing with challenges like variation in pose and appearances. We have used linear support vector machine (SVM) for classification of object and non-object data. With qualitative and quantitative experimental evaluation on standard INRIA Pedestrian dataset, we have compared performance of the proposed method with other state of the art shape feature descriptors based object recognition methods and demonstrated better performance over them.