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.
翻译:在本文中,我们提出了一个基于行人行为模型或代理人的行人轨迹的语义分解方法。 代理人用线性动态模型和轨道的共同起始点和目标位置模拟二维空间行人运动的动态。 首先, 代理人模型是从图象序列的轨迹中估算出来的。 我们的方法建在动态行人代理人混合模型(MDA)的顶部; 但是, MDA的轨迹模型和估计得到改进。 然后, 轨迹被划分为具有语义意义的部分。 轨迹的子组别通过使用估计代理人模型应用隐藏的Markov模型来模拟。 带有真实轨迹数据集的实验结果显示, 与著名的古典拉默- 杜格拉斯- 珀克算法相比, 以及最初的MDA 模型相比, 拟议的方法的有效性。