An AI Federated System for Anomalies Detection in Videos using Convolution Neural Network Mechanism
Keywords:
Machine Learning,, Deep Learning, Surveillance, Abnormal Event Detection, Video Anomaly Detection, Image Processing, Artificial IntelligenceAbstract
Research on this topic has been going on for more than a decade at this point. It focuses on the detection of anomalies in video. Scholars' attention has been drawn to this topic in recent years due to the widespread applicability of its findings. As a direct consequence of this, a wide range of strategies have been utilised over the course of time. These methods range from matrix factorization to methods based on machine learning. Although there are already several studies being conducted in this subject, the purpose of this article is to provide an overview of the recent advancements in the field of detecting anomalies through the use of artificial intelligence and the internet of things (IIoT). Regarding the topic of detecting anomalies in video feeds of a single scene, this article provides a summary of previous research patterns. In this section, we discuss the various ways challenges might be formulated, databases that are open to the public, and criteria for evaluation. In this paper, we implement Convolution Neural Network for detection of anomalies in video. We provide the comparative analysis of these researches on the basis of its accuracy on standard dataset. Apart from that we implement the proposed system based on Convolution Neural Network for Video Anomaly Detection. WMachine Learning,e use the standard performance parameters like, precision, recall, F-Score and accuracy so as to evaluate the performance of our model. For better analysis we compared our model with state of art algorithms like, LR, NB and SVM.
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