Multi-view recognition system for human activity based on multiple features for video surveillance system

Recognition of the activities of human, image sequences is most active area of research in computer vision and most of the previous projects, the focus of the activity is to acknowledge the recognition, from a single view and ignored issues of multiple view invariance. In this paper, the proposed framework for the recognition of a view-invariant human activity, solves the above problem. The components of the proposed framework are three consecutive modules: (i) the detection and positioning of the person’s background subtraction, (ii) the function extraction (iii) and the final activity is referenced by using a set of hidden Markov models (HMMs). During features extraction phase in the proposed method for activity representation a combination of contour-based distance signal feature, optical flow-based motion feature and uniform rotation local binary patterns has been used. Due to its rotation invariant nature the uniform LBP provides view-invariant recognition of multi-view human activities. A successful testing of the proposed approach was done on our own viewpoint dataset, KTH action recognition dataset, i3DPost multi-view dataset, and MSR view-point action dataset. From the experimental results and analysis over the chosen datasets, it is observed that the proposed framework is robust, flexible and efficient with respect to multiple views activity recognition, scale and phase variations.