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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Combining multiple tracking algorithms for improved general performance
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Combining multiple tracking algorithms for improved general performance

机译:结合多种跟踪算法以提高总体性能

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摘要

Automated tracking of objects through a sequence of images has remained one of the difficult problems in computer vision. Numerous algorithms and techniques have been proposed for this task. Some algorithms perform well in restricted environments, such as tracking using stationary camel as, but a general solution is not currently available. A frequent problem is that when an algorithm is refined for one application, it becomes unsuitable for other applications, This paper proposes a general tracking system based on a different approach. Rather than refine one algorithm for a specific tracking task, two tracking algorithms are employed, and used to correct each other during the tracking task. By choosing the two algorithms such that they have complementary failure modes, a robust algorithm is created without increased specialisation. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. [References: 23]
机译:通过一系列图像自动跟踪对象仍然是计算机视觉中的难题之一。已经针对该任务提出了许多算法和技术。一些算法在受限的环境中表现良好,例如使用固定骆驼进行跟踪,但是目前尚无通用解决方案。一个常见的问题是,当一种算法针对一种应用程序进行改进时,它就不适用于其他应用程序。本文提出了一种基于不同方法的通用跟踪系统。而不是针对特定的跟踪任务优化一种算法,而是采用两种跟踪算法,并在跟踪任务期间将其相互纠正。通过选择两种算法,使其具有互补的故障模式,就可以创建鲁棒的算法,而无需增加专业性。 (C)2001模式识别学会。由Elsevier Science Ltd.出版。保留所有权利。 [参考:23]

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