A Review of Real Time Image Processing for Object Detection

Prof. Shivaji Goroba Shinde, Mr. Shubham Suresh Patil
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Abstract

In past days, capture images with very high quality and good size is so easy because of rapid improvement in quality of capturing device with less costly but superior technology. Videos are a collect of sequential images with a constant time interval. So video can provide also more information about our object when scenarios about to changing with respect to time. Therefore, manually handling videosit can be quite impossible. That time all that need an automatic devise to process these videos. In this thesis one such attempt has been made to track objects in videos. Many algorithms and technology have been developed to automate monitoring the object in a video file. Object detection and tracking is a one of the challenging task in computer vision. Mainly there are three basic steps in video analysis: Detection of objects of Interest from moving objects, Tracking of that interested objects in consecutive frames, and Analysis of object tracks to understand their behavior Some common choice to choose suitable feature to categories, visual objects are intensity, shape, color and feature points. In this thesis, we studied about mean shift tracking based on the color pdf, optical flow tracking based on the intensity and motion; SIFT tracking based on scale invariant local feature points. Keywords: real-time, object detection, tracking, surveillance
物体检测的实时图像处理综述
在过去,由于捕捉设备的质量迅速提高,成本较低但技术卓越,因此捕捉高质量、大尺寸的图像变得非常容易。视频是时间间隔不变的连续图像的集合。因此,当场景随时间发生变化时,视频也能提供更多关于物体的信息。因此,手动处理视频是不可能的。这时就需要一种自动装置来处理这些视频。在本论文中,我们就进行了这样的尝试,以跟踪视频中的物体。目前已开发出许多算法和技术来自动监控视频文件中的物体。物体检测和跟踪是计算机视觉领域的一项具有挑战性的任务。视频分析主要有三个基本步骤:从移动物体中检测感兴趣的物体,在连续帧中跟踪感兴趣的物体,以及分析物体轨迹以了解其行为。在本论文中,我们研究了基于颜色 pdf 的均值移动跟踪、基于强度和运动的光流跟踪、基于尺度不变局部特征点的 SIFT 跟踪。关键词: 实时、物体检测、跟踪、监控
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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