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首页> 外文期刊>Multimedia, IEEE Transactions on >Exemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectories
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Exemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectories

机译：示范性AMM：从行人轨迹识别人群运动

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摘要

In this paper, we present a novel method to recognize the types of crowd movement from crowd trajectories using agent-based motion models (AMMs). Our idea is to apply a number of AMMs, referred to as exemplar-AMMs, to describe the crowd movement. Specifically, we propose an optimization framework that filters out the unknown noise in the crowd trajectories and measures their similarity to the exemplar-AMMs to produce a crowd motion feature. We then address our real-world crowd movement recognition problem as a multilabel classification problem. Our experiments show that the proposed feature outperforms the state-of-the-art methods in recognizing both simulated and real-world crowd movements from their trajectories. Finally, we have created a synthetic dataset, SynCrowd, which contains two-dimensional (2D) crowd trajectories in various scenarios, generated by various crowd simulators. This dataset can serve as a training set or benchmark for crowd analysis work.
机译：在本文中，我们提出了一种新颖的方法，可以使用基于代理的运动模型（AMM）从人群轨迹识别人群运动的类型。我们的想法是应用许多称为范例AMM的AMM来描述人群运动。具体而言，我们提出了一种优化框架，该框架可以过滤出人群轨迹中的未知噪声，并测量它们与示例AMM的相似度以产生人群运动特征。然后，我们将现实世界中的人群移动识别问题作为多标签分类问题来解决。我们的实验表明，在从模拟轨迹和真实人群的轨迹中识别出模拟人群和真实人群的运动时，拟议的特征优于最新方法。最后，我们创建了一个综合数据集SynCrowd，其中包含由各种人群模拟器生成的各种场景下的二维（2D）人群轨迹。该数据集可以用作人群分析工作的训练集或基准。

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