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Fuzzy Fusion Approach for Object Tracking

机译:模糊融合的目标跟踪方法

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In multi-target object tracking, for data fusion, data in presence of noise as input must be sent to fusion center to be filtered, associated, combined and made final decision as output. In the chain, association is very important processing. In this paper, an efficient fuzzy logic data association approach for object tracking is proposed. The proposed approach is developed based on the fuzzy clustering means algorithm, which differs from many other fuzzy logic data association algorithms. Performance evaluation and results are reported, and comparisons with other fuzzy logic approaches based on the results described in other reference are also presented. The efficiency of the new approach has been demonstrated by the fuzzy system performance evaluation.
机译:在多目标对象跟踪中,对于数据融合,必须将存在噪声的数据作为输入发送到融合中心,以进行过滤,关联,组合并做出最终决定作为输出。在链中,关联是非常重要的处理。本文提出了一种有效的模糊逻辑数据关联目标跟踪方法。所提出的方法是基于模糊聚类均值算法开发的,该算法不同于许多其他模糊逻辑数据关联算法。报告了性能评估和结果,并根据其他参考文献中描述的结果与其他模糊逻辑方法进行了比较。通过模糊系统性能评估证明了该新方法的有效性。

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