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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 | Size | Format | |
---|---|---|---|---|
7530845.pdf | 717.18 kB | Adobe PDF | ![]() 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 | Size | Format | |
---|---|---|---|---|
7530845.pdf | 717.18 kB | Adobe PDF | ![]() 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 | Size | Format | |
---|---|---|---|---|
7530845.pdf | 717.18 kB | Adobe PDF | ![]() Preview File |