Probabilistic learning and modelling of object dynamics for tracking

T. Tay, K. Sung
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引用次数: 4

Abstract

The problem of tracking can be decomposed and independently addressed in two steps, namely the prediction step and the verification step. In this paper we present a new approach of addressing the prediction step that is based on modelling joint probability densities of successive states of tracked objects. This approach has the advantage that it is conceptually general such that given sufficient training data, it is capable of modelling a wide range of complex dynamics. Furthermore, we show that this conceptual prediction framework can be implemented in a tractable manner using a Gaussian mixture representation which allows predictions to be generated efficiently. We then descibe experiments that demonstrate these benefits.
跟踪目标动力学的概率学习与建模
跟踪问题可以分解为两个步骤,即预测步骤和验证步骤,独立解决。本文提出了一种基于跟踪对象连续状态联合概率密度建模的预测步骤的新方法。这种方法的优点是,它在概念上是通用的,因此,只要有足够的训练数据,它就能够对大范围的复杂动态建模。此外,我们证明了这个概念预测框架可以使用高斯混合表示以一种易于处理的方式实现,从而可以有效地生成预测。然后,我们描述了证明这些好处的实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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