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首页> 外文会议>International Symposium on Systems and Human Science >Tracking People and Action Recognition from Omnidirectional Images
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Tracking People and Action Recognition from Omnidirectional Images

机译：从全向图像跟踪人员和行动识别

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In this chapter, we propose methods for tracking people and recognizing their actions through indoor scenes captured with an omnidirectional image sensor. They can be used to detect and track people to extract their trajectories of movement and their actions are then recognized by using extracted trajectories. Recently, stochastic algorithms have frequently been used for action recognition because they require non-linear and non-Gaussian models of action. Action models prepared from trajectories, however, include movements that are almost the same, so they are redundant. We have, therefore, assumed that human actions can be classified into action primitives, which are modeled by transitions of discrete states considered as action primitives. The methods we propose combine continuous state models and discrete state models by stochastic sampling generated from state transition probabilities.
机译：在本章中，我们提出了跟踪人员的方法，并通过使用全向图像传感器捕获的室内场景来识别其行为。它们可用于检测和跟踪人们提取运动轨迹，然后通过使用提取的轨迹来识别它们的动作。最近，随机算法经常用于动作识别，因为它们需要非线性和非高斯的作用模型。然而，由轨迹准备的动作模型包括几乎相同的动作，因此它们是多余的。因此，我们假设人类行为可以分类为作用原语，其通过被视为动作原语的离散状态的转变来进行建模。我们提出的方法通过状态转换概率产生的随机取样来结合连续状态模型和离散状态模型。

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