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首页> 外文期刊>International journal of intelligent robotics and applications >A novel object tracking method based on a mixture model
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A novel object tracking method based on a mixture model

机译：基于混合的一种新颖的对象跟踪方法模型

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摘要

Object tracking has been applied in many fields such as intelligent surveillance and computer vision. Although much progress has been made, there are still many puzzles which pose a huge challenge to object tracking. Currently, the problems are mainly caused by occlusion, similar object appearance and background clutters. A novel method based on a mixture model was proposed for solving these issues. The mixture model was integrated into a Bayes framework with the combination of locally dense contexts feature and the fundamental image information (i.e. the relationship between the object and its surrounding regions). This is because that the tracking problem can be seen as a prediction question, which can be solved using the Bayes method. In addition, both scale variations and templet updating are considered to assure the effectiveness of the proposed algorithm. Furthermore, the Fourier Transform (FT) is used when solving the Bayes equation to make the algorithm run in a real-time system. Therefore, the MMOT (Mixture model for object tracking) can run faster and perform better than existing algorithms on some challenging images sequences in terms of accuracy, quickness and robustness.
机译：对象跟踪已经应用在许多领域如智能监测和计算机愿景。仍有许多难题造成巨大的挑战对象跟踪。问题主要是由阻塞引起的,相似的对象的外观和背景杂波。基于混合模型的新方法提出了解决这些问题。模型集成到一个贝叶斯框架局部密集的结合上下文特征(即,和基本的图像信息对象及其之间的关系周围的地区)。跟踪问题可以被看作是一个预测使用贝叶斯问题,可以解决方法。模板更新是保证该算法的有效性。此外,使用傅里叶变换(FT)当解决贝叶斯方程使算法在实时系统中运行。MMOT(混合模型对象跟踪)跑得更快并执行比现有的更好算法在一些具有挑战性的图像序列的准确性、速度和鲁棒性。

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