Multi-object tracking using Kalman filter and particle filter

Chetan M. Bukey, Shailesh.V. Kulkarni, Rohini Chavan
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引用次数: 10

Abstract

Tracking Object is essential step for image and video processing research area and in computer vision technology applications like object identification, traffic control, automated surveillance systems and navigation systems. Foreground image separated from background image by conventionally image processing techniques. Background subtractions utilizing Gaussian Mixture Model (GMM) is basically utilized as a part of extricating elements of moving items and takes information in frames. The outcome demonstrates that GMM performs well when obstructions are there. Multiple objects tracking have been done using two methods that is Kalman filter (KF) tracking and the Particle filter (PF) tracking. The KF evaluate present, previous, and even future condition of object. Also Kalman filter can estimate even when exact idea of the demonstrated framework is unknown. PF have been being exceptionally helpful in multiple objects tracking for non-Gaussian and nonlinear estimation problems. The algorithm applied effectively on standard video database of PETS.
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基于卡尔曼滤波和粒子滤波的多目标跟踪
跟踪目标是图像和视频处理研究领域以及物体识别、交通控制、自动监控系统和导航系统等计算机视觉技术应用的重要步骤。通过传统的图像处理技术将前景图像与背景图像分离。基于高斯混合模型(GMM)的背景减法基本上是作为提取运动物体元素的一部分,并在帧中获取信息。结果表明,GMM在障碍物存在时表现良好。多目标跟踪主要采用卡尔曼滤波(KF)和粒子滤波(PF)两种方法。KF评估对象现在、过去甚至未来的状态。此外,卡尔曼滤波可以在不知道所演示框架的确切思想时进行估计。在非高斯和非线性估计问题的多目标跟踪中,PF一直是非常有用的。该算法在pet标准视频数据库上得到了有效的应用。
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