Optimization of real-time video processing algorithms for surveillance systems
Table Of Contents
<p>1. Introduction<br> 1.1 Background and Motivation<br> 1.2 Objectives and Scope<br> 1.3 Challenges in Real-Time Video Processing for Surveillance<br>2. Literature Review<br> 2.1 Video Processing Algorithms for Surveillance<br> 2.2 Real-Time Object Detection and Tracking<br> 2.3 Performance Metrics for Surveillance Systems<br>3. Algorithm Optimization<br> 3.1 Object Detection and Recognition<br> 3.2 Motion Tracking and Analysis<br> 3.3 Real-Time Video Analytics<br>4. Experimental Setup<br> 4.1 Surveillance Camera and Video Dataset<br> 4.2 Benchmarking and Performance Evaluation<br>5. Optimization Techniques<br> 5.1 Parallel Processing and Hardware Acceleration<br> 5.2 Algorithmic Optimization and Feature Extraction<br> 5.3 Latency Reduction and Streamlining<br></p>
Thesis Abstract
<p> This project aims to optimize real-time video processing algorithms for surveillance systems to enhance the accuracy, speed, and efficiency of video analytics. The project will focus on improving object detection, tracking, and behavior analysis algorithms to enable more effective monitoring and threat detection in surveillance applications. The goal is to achieve real-time processing of high-definition video streams with minimal latency and improved detection performance. <br></p>Thesis Overview
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