Difference intensity distance group pattern for recognizing actions in video using Support Vector Machines

Abstract

Recognition of human actions is a very important, task in many applications such as Human Computer Interaction, Content based video retrieval and indexing, Intelligent video surveillance, Gesture Recognition, Robot learning and control, etc. An efficient action recognition system using Difference Intensity Distance Group Pattern (DIDGP) method and recognition using Support Vector Machines (SVM) classifier is presented. Initially, Region of Interest (ROI) is extracted from the difference frame, where it represents the motion information. The extracted ROI is divided into two blocks B1 and B2. The proposed DIDGP feature is applied on the maximum intensity block of the ROI to discriminate the each action from video sequences. The feature vectors obtained from the DIDGP are recognized using SVM with polynomial and RBF kernel. The proposed work has been evaluated on KTH action dataset which consists of actions like walking, running, jogging, hand waving, clapping and boxing. The proposed method has been experimentally tested on KTH dataset and an overall accuracy of 94.67% for RBF kernel.