论文标题
使用非参数轨迹聚类和离散过渡点的行人运动模型
Pedestrian Motion Model Using Non-Parametric Trajectory Clustering and Discrete Transition Points
论文作者
论文摘要
本文提出了一个行人运动模型，其中包括低级轨迹模式和高级离散过渡。与当前的最新水平相比，这两个级别的包含都会创建一个更一般的预测模型，从而可以对行人轨迹进行更有意义的预测和推理。该模型使用具有（1）dirichlet过程高斯过程的迭代聚类算法将轨迹群集成连续运动模式和（2）假设测试，以识别称为过渡点的数据中的离散过渡。该模型基于过渡点，将完整的轨迹划分为子区域群集簇，其中行人做出离散决策。然后在过渡点和轨迹簇上学习状态过渡概率。该模型用于在线预测动作和检测异常轨迹。提出的模型在杜克MTMC数据集上进行了验证，以证明对低级轨迹簇和高级过渡的识别，以及预测行人运动和以高精度在线检测异常的能力。
This paper presents a pedestrian motion model that includes both low level trajectory patterns, and high level discrete transitions. The inclusion of both levels creates a more general predictive model, allowing for more meaningful prediction and reasoning about pedestrian trajectories, as compared to the current state of the art. The model uses an iterative clustering algorithm with (1) Dirichlet Process Gaussian Processes to cluster trajectories into continuous motion patterns and (2) hypothesis testing to identify discrete transitions in the data called transition points. The model iteratively splits full trajectories into sub-trajectory clusters based on transition points, where pedestrians make discrete decisions. State transition probabilities are then learned over the transition points and trajectory clusters. The model is for online prediction of motions, and detection of anomalous trajectories. The proposed model is validated on the Duke MTMC dataset to demonstrate identification of low level trajectory clusters and high level transitions, and the ability to predict pedestrian motion and detect anomalies online with high accuracy.
