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首页> 外文期刊>Journal of visual communication & image representation >Heuristic algorithm for visual tracking of deformable objects
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Heuristic algorithm for visual tracking of deformable objects

机译：视觉跟踪可变形物体的启发式算法

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摘要

Many vision problems require fast and accurate tracking of objects in dynamic scenes. These problems can be formulated as exploration problems and thus can be expressed as a search into a state space based approach. However, these problems are hard to solve because they involve search through a space of transformations corresponding to all the possible motion and deformation. In this paper, we propose a heuristic algorithm through the space of transformations for computing target 2D motion. Three features are combined in order to compute efficient motion: (1) a quality of function match based on a holistic similarity measurement, (2) Kullback-Leibler measure as heuristic to guide the search process and (3) incorporation of target dynamics into the search process for computing the most promising search alternatives. Once 2D motion has been calculated, the result value of the quality of function match computed is used with the purpose of verifying template updates. A template will be updated only when the target object has evolved to a transformed shape dissimilar with respect to the actual shape. Also, a short-term memory subsystem is included with the purpose of recovering previous views of the target object. The paper includes experimental evaluations with video streams that illustrate the efficiency and suitability for real-time vision based tasks in unrestricted environments.
机译：许多视觉问题需要快速准确地跟踪动态场景中的对象。这些问题可以表述为探索问题，因此可以表示为对基于状态空间的方法的搜索。但是，这些问题很难解决，因为它们涉及到搜索与所有可能的运动和变形相对应的变换空间。在本文中，我们提出了一种通过变换空间的启发式算法来计算目标2D运动。为了计算有效运动，将三个特征进行了组合：（1）基于整体相似性度量的功能匹配质量；（2）启发式Kullback-Leibler度量用于指导搜索过程；（3）将目标动力学纳入搜索过程，用于计算最有希望的搜索替代方案。一旦计算出2D运动，就将计算出的功能质量匹配的结果值用于验证模板更新。仅当目标对象已演变为与实际形状不同的变形形状时，才会更新模板。此外，出于恢复目标对象的先前视图的目的，还包括短期内存子系统。本文包括视频流的实验评估，这些视频流说明了在不受限制的环境中基于实时视觉的任务的效率和适用性。

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