一种基于K-NN的人跟踪新方法:与Sift和Mean Shift方法的比较

Asmaa Ait Moulay, A. Amine
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引用次数: 0

摘要

对象跟踪可以定义为从视频场景中检测感兴趣的对象并跟踪其运动,方向,遮挡等以提取有用信息的过程。这确实是一个具有挑战性的问题,也是一项重要的任务。计算机视觉领域,特别是视频监控中的目标跟踪领域吸引了众多研究者。本文的主要目的是向读者介绍目标跟踪的现状,同时介绍背景减法所涉及的步骤及其技术。在相关文献中,我们发现了三种主要的目标跟踪方法:第一种方法是光流;第二种是与背景减法相关的,本文将背景减法分为两种,其次是时间差分法和SIFT法,最后一种是均值移位法。我们提出了一种新的背景减法方法,将当前帧与我们之前设置的背景模型进行比较,这样我们就可以将图像的每个像素分类为前景或背景元素,然后通过他的质心来呈现我们感兴趣的对象,这是一个人。本文将跟踪步骤分为两种不同的方法:曲面法和K-NN法,并对两种方法进行了说明。利用CAVIAR数据库对该方法进行了实现和评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Method for Person Tracking Based K-NN : Comparison with Sift and Mean Shift Method
Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful information. It is indeed a challenging problem and it’s an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, then the temporal differencing and the SIFT method and the last one is the mean shift method. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper. Our proposed method is implemented and evaluated using CAVIAR database.
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