对象和多对象跟踪系统建模

Afef Salhi, Yacine Moresly, F. Ghozzi, Ameni Yengui, A. Fakhfakh
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引用次数: 3

摘要

本文介绍了一种实现对象跟踪和多目标检测的系统。对视频序列中运动物体的不同跟踪算法进行了分析。对特定目标进行精确、鲁棒、高效、可靠、快速的跟踪是一个难以解决的问题。因此,它在计算机视觉应用中是非常必要的。因此,自20世纪70年代以来,跟踪视频序列中的目标是一个非常活跃的研究领域。它已经吸引了许多人在研究领域的各种应用。跟踪通常是分析活动、检测、行为、交互和感兴趣对象之间关系的第一步。人们提出并发展了许多跟踪目标的方法。跟踪是通过在视频序列所在的帧中运动来估计和分析帧平面中物体的轨迹。大多数运动对象跟踪算法采用固定摄像机捕获的输入帧来输出视频序列。然而,还有其他算法将视频序列作为输入,在输出时提供包含跟踪对象的视频。这些算法执行检测对象的第一步,以确定当前帧的哪些像素属于序列的背景,哪些像素代表运动对象。目标跟踪问题可以表示为在视频序列的每一帧中检测目标。整套应用(安防系统、军事系统、智能交通系统、视频会议、监控等)都可以解决。因此,系统的轻巧也让你可以打开最近的应用程序,如人机界面和/或人机界面,其中传感器可以嵌入到移动机器人上。这通常需要利用运动中的对象跟踪技术和算法,这些技术和算法将在这些接口中引入的FPGA目标上注册和实现。我们可以举一个例子,然后在文献中现有的主要算法,如块匹配,KLT,卡尔曼滤波,Meanshift和Camshift。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling from an Object and Multi-object Tracking System
This paper describes an implementation system tracking and detection object and multi-object. We trait the different tracking algorithms of moving objects in video sequences. The tracking a particular precise object, robust, efficient, reliable and fast is a difficult problem to solve. Hence, it is a very essential for computer vision applications. For this reason, tracking objects in video sequences is a very active area of research since the 1970. It has attracted many people in the area of research for use in a variety of applications. The tracking is often the first step in an analysis of the activities, detection, behavior, interactions and relationships between objects of interest. Many methods of tracking objects have been proposed and developed. Tracking is the estimation and analysis of the trajectories of an object in the frame plane by moving in an frame where video sequence. Most of the motion objects tracking algorithms take input frames captured by a fixed camera to give at output a video sequence. However, there are other algorithms that take as input a video sequence to provide a video at output containing the tracked objects. These algorithms perform a first step of the detection objects in order to determine which of the pixels of the current frame which belong to the background of the sequence and which represent the motion objects. The problem of object tracking can be expressed in terms of detection of the object in each frame of the video sequence. The set of applications (security systems, military systems, intelligent transportation systems, video conferencing, surveillance, etc.) can be addressed. So, the lightness of the systems also lets you open recent applications such as human-machine interface and/or the human-robot interface, where the sensors can be embedded on a mobile robot. This usually requires the exploitation of object tracking techniques and algorithms in motion that will register and implement on FPGA targets that are introduced in these interfaces. We can cite as an example then the main existing algorithms in the literature as the block-matching, the KLT, the Kalman filter, the Meanshift and the Camshift.
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