计算机视觉中运动行为分析就是在不需要人为干预的情况下,综合利用计算机视觉、模式识别、图像处理、人工智能等诸多方面的知识和技术对摄像机拍录的图像序列进行自动分析,实现动态场景中的人体定位、跟踪和识别,并在此基础上分析和判断人的行为,其最终目标是通过对行为特征数据的分析来获取行为的语义描述与理解。运动人体行为分析在智能视频监控、高级人机交互、视频会议、基于行为的视频检索以及医疗诊断等方面有着广泛的应用前景和潜在的商业价值,是近年来计算机视觉领域最活跃的研究方向之一。 它包含视频中运动人体的自动检测、行为特征提取以及行为理解和描述等,属于图像分析和理解的范畴。从技术角度讲,人体行为分析和识别的研究内容相当丰富,涉及到图像处理、计算机视觉、模式识别、人工智能、形态学等学科知识。

In this paper, we propose a method for semantic segmentation of pedestrian trajectories based on pedestrian behavior models, or agents. The agents model the dynamics of pedestrian movements in two-dimensional space using a linear dynamics model and common start and goal locations of trajectories. First, agent models are estimated from the trajectories obtained from image sequences. Our method is built on top of the Mixture model of Dynamic pedestrian Agents (MDA); however, the MDA's trajectory modeling and estimation are improved. Then, the trajectories are divided into semantically meaningful segments. The subsegments of a trajectory are modeled by applying a hidden Markov model using the estimated agent models. Experimental results with a real trajectory dataset show the effectiveness of the proposed method as compared to the well-known classical Ramer-Douglas-Peucker algorithm and also to the original MDA model.

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