首页> 外文会议>Conference on Signal Processing, Sensor Fusion, and Target Recognition Ⅹ Apr 16-18, 2001, Orlando, USA >Tracking of Moving Objects in Scenery using Subspace Projection using Independent Component Analysis
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Tracking of Moving Objects in Scenery using Subspace Projection using Independent Component Analysis

机译:使用独立分量分析的子空间投影跟踪风景中的运动对象

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A system is developed for tracking moving objects through natural scenery. A technique is presented for performing change detection on imagery to determine the difference between two images or a sequence of images. From there an algorithm is presented to detect the presence of a new object and/or the deletion of objects. Then the application of a Variable Structure Interacting Multiple Model tracking filter is presented. The method of performing change detection is based upon the concept of image subspace projection. A set of "basis" image maps are formed when combined with a mixing matrix can recreate the original image. The subsequent images are then projected into the base image. The projected image is then subtracted from the original image to perform the change detection. Spatial Filtering is applied to increase the contrast between the change and the background then an adaptive filter is then applied to pass the locations of changes in the images into the tracking filter. Tracking is performed through the use of multiple motion models. The filter's motion models are adaptive added or deleted as required by the moving object's dynamics. The moving object's state is estimated through extended Kalman filtering.
机译:开发了一种用于跟踪自然景观中移动物体的系统。提出了一种用于对图像执行变化检测以确定两个图像或图像序列之间的差异的技术。从那里提出一种算法来检测新对象的存在和/或对象的删除。然后介绍了一种可变结构交互多模型跟踪滤波器的应用。执行变化检测的方法基于图像子空间投影的概念。当与混合矩阵结合可以重新创建原始图像时,会形成一组“基本”图像图。然后将后续图像投影到基础图像中。然后从原始图像中减去投影图像以执行变化检测。应用空间滤波以增加变化和背景之间的对比度,然后应用自适应滤波器将图像中变化的位置传递到跟踪滤波器中。通过使用多个运动模型执行跟踪。过滤器的运动模型可以根据运动对象的动力学进行自适应添加或删除。通过扩展卡尔曼滤波来估计运动对象的状态。

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