Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/587433
Title: Exemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectories
Authors: Wenxi Liu;Rynson W. H. Lau;Xiaogang Wang;Dinesh Manocha
subject: Crowd behavior modeling|pattern recognition|crowd simulation|video surveillance
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/172248
http://localhost/handle/Hannan/587433
ISSN: 1520-9210
1941-0077
volume: 18
issue: 12
Appears in Collections:2016

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Title: Exemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectories
Authors: Wenxi Liu;Rynson W. H. Lau;Xiaogang Wang;Dinesh Manocha
subject: Crowd behavior modeling|pattern recognition|crowd simulation|video surveillance
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/172248
http://localhost/handle/Hannan/587433
ISSN: 1520-9210
1941-0077
volume: 18
issue: 12
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7530845.pdf717.18 kBAdobe PDFThumbnail
Preview File
Title: Exemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectories
Authors: Wenxi Liu;Rynson W. H. Lau;Xiaogang Wang;Dinesh Manocha
subject: Crowd behavior modeling|pattern recognition|crowd simulation|video surveillance
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/172248
http://localhost/handle/Hannan/587433
ISSN: 1520-9210
1941-0077
volume: 18
issue: 12
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7530845.pdf717.18 kBAdobe PDFThumbnail
Preview File