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Representing And Recognizing Motion Trajectories: A Tube And Droplet Approach

Authors:

Hang Su,

Yu ZhouAuthors Info & Claims

MM '14: Proceedings of the 22nd ACM international conference on Multimedia

Pages 1077 - 1080

https://doi.org/10.1145/2647868.2654955

Published: 03 November 2014 Publication History

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Abstract

This paper addresses the problem of representing and recognizing motion trajectories. We first propose to derive scene-related equipotential lines for points in a motion trajectory and concatenate them to construct a 3D tube for representing the trajectory. Based on this 3D tube, a droplet-based method is further proposed which derives a "water droplet" from the 3D tube and recognizes trajectory activities accordingly. Our proposed 3D tube can effectively embed both motion and scene-related information of a motion trajectory while the proposed droplet- based method can suitably catch the characteristics of the 3D tube for activity recognition. Experimental results demonstrate the effectiveness of our approach.

References

[1]

J. Nascimento, M. Figueiredo, and J. Marques, "Trajectory classification using switched dynamical hidden Markov models," IEEE Trans. Image Proc., pp. 1338--1348, 2010.

Digital Library

Google Scholar

[2]

H. Chu, W. Lin, J. Wu, X. Zhou, Y. Chen, and H. Li, "A new heat-map-based algorithm for human group activity recognition," ACM Multimedia (MM), pp. 1069--1072, 2012.

Digital Library

Google Scholar

[3]

K. Kim, D. Lee, and I. Essa, "Gaussian process regression flow for analysis of motion trajectories," ICCV, 2011.

Google Scholar

[4]

C. Rao, A. Yilmaz, and M. Shah, "View-invariant representation and recognition of actions," IJCV, 2002.

Digital Library

Google Scholar

[5]

D. Ellis, E. Sommerlade, and I. Reid, "Modelling pedestrian trajectory patterns with gaussian processes," Int. Conf. Computer Vision Workshops, pp. 1229--1234, 2009.

Google Scholar

[6]

W. Lin, Y. Chen, J. Wu, H. Wang, B. Sheng, and H. Li, "A new network-based algorithm for human activity recognition in videos," IEEE Trans. CSVT, 24(5), pp. 826--841, 2014.

Google Scholar

[7]

V. Streeter, E. Wylie, and K. Bedford, "Fluid Mechanics," McGraw-Hill press, 1987.

Google Scholar

[8]

C. Chang and C. Lin. "LIBSVM: a library for support vector machines," ACM Trans. Intell. Syst. Tech., pp. 1--27, 2011.

Digital Library

Google Scholar

[9]

R. Hess and A. Fern, "Discriminatively trained particle filters for complex multi-object tracking," CVPR, 2009.

Google Scholar

[10]

X. Wang, K. T. Ma, G. Ng, and E. Grimson, "Trajectory analysis and semantic region modeling using nonparametric hierarchical bayesian models, " IJCV, 2011.

Digital Library

Google Scholar

[11]

B. Morris and M. Trivedi, "Trajectory learning for activity understanding: unsupervised, multilevel, and long-term adaptive approach," IEEE Trans. PAMI, 2011.

Digital Library

Google Scholar

Cited By

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Zhong ZLuo YLiang W(2022)STGM: Vehicle Trajectory Prediction Based on Generative Model for Spatial-Temporal FeaturesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.316064823:10(18785-18793)Online publication date: Oct-2022
https://doi.org/10.1109/TITS.2022.3160648
Hou LXin LLi SCheng BWang W(2020)Interactive Trajectory Prediction of Surrounding Road Users for Autonomous Driving Using Structural-LSTM NetworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.294208921:11(4615-4625)Online publication date: Nov-2020
https://doi.org/10.1109/TITS.2019.2942089
(2015)Summarizing surveillance videos with local-patch-learning-based abnormality detection, blob sequence optimization, and type-based synopsisNeurocomputing10.1016/j.neucom.2014.12.044155:C(84-98)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1016/j.neucom.2014.12.044

Index Terms

Representing And Recognizing Motion Trajectories: A Tube And Droplet Approach
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Video summarization

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Published In

MM '14: Proceedings of the 22nd ACM international conference on Multimedia

November 2014

1310 pages

ISBN:9781450330633

DOI:10.1145/2647868

General Chairs:
Kien A. Hua
University of Central Florida, USA
,
Yong Rui
Microsoft Research, China
,
Ralf Steinmetz
Technische Universitt Darmstadt, Germany
,
Program Chairs:
Alan Hanjalic
Delft University of Technology, Netherlands
,
Apostol (Paul) Natsev
Google, USA
,
Wenwu Zhu
Tsinghua University, China

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2014

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Shanghai Pujiang Program
National Natural Science Foundation of China
Ministry of Science and Technology of the People's Republic of China
Shanghai Jiao Tong University

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MM '14

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SIGMM

MM '14: 2014 ACM Multimedia Conference

November 3 - 7, 2014

Florida, Orlando, USA

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Zhong ZLuo YLiang W(2022)STGM: Vehicle Trajectory Prediction Based on Generative Model for Spatial-Temporal FeaturesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.316064823:10(18785-18793)Online publication date: Oct-2022
https://doi.org/10.1109/TITS.2022.3160648
Hou LXin LLi SCheng BWang W(2020)Interactive Trajectory Prediction of Surrounding Road Users for Autonomous Driving Using Structural-LSTM NetworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.294208921:11(4615-4625)Online publication date: Nov-2020
https://doi.org/10.1109/TITS.2019.2942089
(2015)Summarizing surveillance videos with local-patch-learning-based abnormality detection, blob sequence optimization, and type-based synopsisNeurocomputing10.1016/j.neucom.2014.12.044155:C(84-98)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1016/j.neucom.2014.12.044

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Extraction and temporal segmentation of multiple motion trajectories in human motion

Human activity recognition using motion trajectories

A Tube-and-Droplet-Based Approach for Representing and Analyzing Motion Trajectories

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