An Efficient Multi-view Based Activity Recognition System for Video Surveillance Using Random Forest

Vision-based human activity recognition is an emerging field and have been actively carried out in computer vision and artificial intelligence area. However, human activity recognition in a multi-view environment is a challenging problem to solve, the appearance of a human activity varies dynamically, depending on camera viewpoints. This paper presents a novel and proficient framework for multi-view activity recognition approach based on Maximum Intensity Block Code (MIBC) of successive frame difference. The experimare carried out using West Virginia University (WVU) multi-view activity dataset and the extracted MIBC features are used to train Random Forest for classification. The experimental results exhibit the accuracies and effectiveness of the proposed method for multi-view human activity recognition in order to conquer the viewpoint dependency. The main contribution of this paper is the application of Random Forests classifier to the problem of multi-view activity recognition in surveillance videos, based only on human motion.