63 Intelligent Video Surveillance Using Deep Learning System Py063

63 Intelligent Video Surveillance Using Deep Learning System Py063 This document describes an intelligent video surveillance system that uses deep learning techniques to detect abnormal activities in videos and in real time. the system analyzes uploaded videos or live camera feeds with a spatial autoencoder model to identify abnormalities like violence or theft. Surveillance videos are a major contribution to unstructured big data. the main objective of this paper is to give brief about video analysis using deep learning techniques in order to detect suspicious activities.

Deep Surveillance With Deep Learning Intelligent Video Surveillance This study presents the expansion and experimental evaluation of an intelligent video surveillance system that integrates facial recognition, activity recognition and weapon detection using recurrent neural networks (rnns), convolutional neural networks (cnns) and yolo based models. Recent improvements in computer vision, particularly deep learning approaches, have opened up new possibilities for these systems, enhancing their capabilities and launching new research areas in this field. An advanced video surveillance system that uses deep learning to detect and classify various types of criminal activities in real time. the system provides whatsapp alerts for critical security events. Current activity recognition techniques are using convolutional neural network (cnn) model with computationally complex classifiers, creating hurdles in obtaining quick responses for abnormal activity, so this paper proposes a framework for activity detection.

An Optimal Intelligent Video Surveillance System In Object Detection An advanced video surveillance system that uses deep learning to detect and classify various types of criminal activities in real time. the system provides whatsapp alerts for critical security events. Current activity recognition techniques are using convolutional neural network (cnn) model with computationally complex classifiers, creating hurdles in obtaining quick responses for abnormal activity, so this paper proposes a framework for activity detection. The proposed intelligent video surveillance system effectively combines yolo and rcnn models to detect critical threats such as guns, fire, refill actions, and abnormal behaviors in real time. Surveillance videos are a major contribution to unstructured big data. the main objective of this paper is to give brief about video analysis using deep learning techniques in order to. An important step for surveillance activity recognition is to detect, localize, and track each individual throughout the video stream. this task is not feasible with object detectors that are trained on general categories of data. This research presents the development of an intelligent video surveillance system using deep learning techniques, specifically employing the yolov5s object detection model for efficient and accurate real time detection of objects such as people, vehicles, and animals.