Vehicle Detection and Count in the Captured Stream Video Using Opencv in Machine Learning

Milon Rana, Tajkuruna Akter Tithy, Madina Hasan
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引用次数: 2

Abstract

The technology of detection within the captured video has implementation within the sort of fields. This emerging technology when implemented over the real-time video feeds could even be beneficial. The supreme good thing about vehicle detection within the real-time streaming video feed is to trace vehicles in busy roads or Bridges like Padma or Jamuna Bridge. An accidents occurred anywhere which may rather be detected. Vehicle detection also called computer vision beholding, basically the scientific methods and ways of how machines see instead of human eyes. This chapter aims to explore the prevailing challenging issue within the planet of unsupervised surveillance and security, Helps traffic police, Maintaining records and Traffic surveillance control. The detection of vehicles is implemented with enhanced algorithms and machine learning libraries like OpenCV, TensorFlow, and others. The varied approaches are accustomed identify and track the particular object through the trained model from the captured video.
机器学习中使用Opencv捕获流视频中的车辆检测和计数
在采集的视频中进行检测的技术可以在分类领域内实现。这种新兴技术在实现实时视频馈送时甚至可能是有益的。在实时流媒体视频馈送中,车辆检测的最大好处是追踪繁忙道路或桥梁上的车辆,如帕德玛或贾穆纳桥。事故发生在任何可能被发现的地方。车辆检测也被称为计算机视觉观察,基本上是机器代替人眼观察的科学方法和方式。本章旨在探讨在无人监督的监视和安全星球上普遍存在的具有挑战性的问题,帮助交通警察,维护记录和交通监视控制。车辆检测是通过增强的算法和机器学习库(如OpenCV、TensorFlow等)实现的。通过训练模型从捕获的视频中识别和跟踪特定的目标,习惯了各种方法。
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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