Activity Recognition System for Smart Campus

Abstract

Abstract: Long video data resulted from the use of cameras lab activities as a action recording. We present a new framework for recognizing student activities from the class. In this system we improve intelligent mechanism, top low level motion detection algorithm and feature extraction. We recognize the frame difference and feature selection for human activities that permits recognition. The detection of human activity from videos is very complicated appropriate to the complex reality of events, the situation in which activities took place, the require of available size of abnormal ground truth training data and other factors correlated to environmental disparity, light conditions and the working position of the captured cameras. The objective of this paper is to research and inspect machine and deep learning techniques by using videos for recognition of students lab activities . The importance has been on a variety of activity detection systems with machine learning techniques as their prime move toward