用于安全监控的静态障碍物检测

G. Shanmugasundaram, Dr. C. PunithaDevi, S. Balaji, T. Mugilan
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引用次数: 0

摘要

最近,我们听到很多意外事件发生在拥挤的地方,特别是在印度这样的国家,爆炸物藏在被遗弃的行李或留下的物品中。即使安装了安全摄像头,在正确的时间进行监控并发出警报仍然是一项手动活动,这会导致错误和延迟检测。随着监控摄像机电子和图像处理算法(如Haar级联分类器算法,支持向量机(SVM)等)的进步,可以使用深度学习对象检测来检测图像,基于特征的对象检测可以自动进行此类检测。本文的目的是探讨各种因素及其在目标检测中的重要性。此外,它还探讨了现有的技术和模型用于检测的对象。本文总结了目标检测中尚未解决的各种挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
STATIC OBSTACLE DETECTION FOR SECURITY IN SURVEILLANCE
In recent times, we hear a lot of unexpected incidents happening in crowded places carried out by explosives hidden inside abandoned baggage or left behind items especially in India like country. Even though security cameras are placed, which monitors and raise alarms at the right time is still a manual activity that leads to mistakes and delayed detection. With the advancement in surveillance camera electronics and image processing algorithms such as Haar cascade classifier algorithm, Support Vector Machine (SVM), etc., and images can be detected using Deep learning object detection ,Feature- based object detection where it is possible to automate such detection. The objective of this article is to explore a various factors and its importance in object detection. Further it also explores about the existing techniques and models used in detection of objects. This article concludes with various challenges in object detection which are not yetaddressed.
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