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首页> 外文期刊>Transactions of the Institute of Measurement and Control >Performance improvement in artificial intelligence-based objects tracking via probabilistic estimation approach
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Performance improvement in artificial intelligence-based objects tracking via probabilistic estimation approach

机译：通过概率估计方法提高基于人工智能的对象跟踪的性能

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摘要

The present research is focused on the object tracking domain based upon a new probabilistic estimation approach. This is realized through a generalization of the particle filter framework (PFF), in association with a neural network, namely an intelligence-based PFF (IPFF). In this idea, a number of appropriate features of mobile objects should first be considered for use in the process of IPFF realization to make the estimation and better performance. The applicability of the proposed approach has been considered though three separated scenarios, including non-stationary, stationaryon-stationary and finally stationary objects, as long as the standard mean shift object tracking approach is realized as a benchmark approach. The experimental results verify the approach performance improvement.
机译：本研究集中在基于一种新的概率估计方法的对象跟踪域上。这是通过将粒子过滤器框架（PFF）与神经网络（即基于智能的PFF（IPFF））相关联来实现的。在这种想法下，首先应考虑在IPFF实现过程中使用移动对象的许多适当功能，以进行估计并获得更好的性能。只要将标准均值移位对象跟踪方法实现为基准方法，就可以通过三种分离的方案（包括非平稳，静止/非静止以及最终静止的对象）来考虑所提出方法的适用性。实验结果验证了方法性能的提高。

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