Design and Implementation of Anomaly Detection in Video Surveillance using Foreground Detection

Abnormal event detection is now a challenging task, especially for crowded scenes. Many existing methods learn a normal event model in the training phase, and events which cannot be well represented are treated as abnormalities. It fails to make use of abnormal event patterns, which are elements to comprise abnormal events. Moreover, normal patterns in testing videos may be divergent from training ones, due to the existence of abnormalities. Anomaly detection finds extensive use in a wide variety of applications such as fraud detection for credit cards, insurance or health care, intrusion detection for cyber-security, fault detection in safety critical systems, and military surveillance for enemy activities. The proposed detector treats each sample as a combination of a set of event patterns. Due to the unavailability of labeled abnormalities for training, abnormal patterns are adaptively extracted from incoming unlabeled testing samples. It detects the moving object using Gaussian Mixture Model based on foreground detection and the abnormalities in the videos are detected. Citation: Maragathameenakshi M, Sri Muthukumaran Institute of Technology; Nivedha S ,Sri Muthukumaran Institute of Technology; Mr. B Venkataramanaiah ,Sri Muthukumaran Institute of Technology. "Design and Implementation of Anomaly Detection in Video Surveillance using Foreground Detection." Global Research and Development Journal For Engineering : 19 - 24.