A Review Paper on Real-Time Video Analysis in Dense Environment for Surveillance System

Himanshu Tyagi, Vivek Kumar, Manish Kumar
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Abstract

Dense environmental conditions such as snow, fog, lightning, heavy rain, and darkness drastically lower the quality of outdoor surveillance videos. The primary functions of video surveillance systems in crowded environments have received significant attention, particularly in detection, categorization, and event or object recognition. The methods and algorithms for real-time video analysis in various weather conditions have also significantly advanced with the advancement of technology. Examples include background extraction, the see-through algorithm, deep learning models, CNN for nighttime intrusions, the System for high-quality underwater Monitoring using optical-wireless video surveillance, the low-visibility enhancement network (LVENet), edge computing, and many others. Using various elements of these methodologies, the current research increased monitoring performance and avoided potential human failures. In-depth information about these video surveillance methods, systems, and supporting details is provided in this study. An overview of employed construction and architectural styles is given, and the critical assessments of these systems are then covered. Current surveillance systems and various methods for achieving accuracy in real-time video analysis in adverse weather circumstances are contrasted in terms of their features, benefits, and challenges, which are discussed in this paper, to provide a complete image and a broad view of the System. Future trends are also highlighted, pointing to the study that will be conducted in the future.
密集环境下监控系统实时视频分析研究综述
雪、雾、闪电、大雨、黑暗等密集的环境条件会大大降低室外监控视频的质量。视频监控系统在拥挤环境中的主要功能受到了极大的关注,特别是在检测、分类和事件或物体识别方面。随着技术的进步,各种天气条件下实时视频分析的方法和算法也有了显著的进步。例子包括背景提取、透视算法、深度学习模型、夜间入侵CNN、使用光学无线视频监控的高质量水下监控系统、低可见度增强网络(LVENet)、边缘计算等。使用这些方法的各种元素,目前的研究提高了监测性能并避免了潜在的人为故障。本研究提供了有关这些视频监控方法、系统和支持细节的深入信息。所采用的建设和建筑风格的概述给出,并对这些系统的关键评估,然后涵盖。目前的监控系统和各种方法来实现准确的实时视频分析在恶劣的天气条件下,在他们的特点,优点和挑战方面进行了对比,在本文中讨论,以提供一个完整的图像和系统的广阔视野。还强调了未来的趋势,指出了将在未来进行的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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