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A new heat-map-based algorithm for human group activity recognition

Published: 29 October 2012 Publication History

Abstract

In this paper, a new heat-map-based (HMB) algorithm is proposed for human group activity recognition. The proposed algorithm first models people trajectories as series of "heat sources" and then applies a thermal diffusion process to create a heat map (HM) for representing the group activities. Based on this heat map, a new surface-fitting (SF) method is also proposed for recognizing human group activities. Our proposed HM feature can efficiently keep the temporal motion information of the group activities while the proposed SF method can effectively catch the characteristics of the heat map for activity recognition. Experimental results demonstrate the effectiveness of our proposed algorithm.

References

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Y. Zhou, S. Yan, and T. Huang, "Pair-activity classification by bi-trajectory analysis," IEEE Conf. Computer Vision and Pattern Recogntion (CVPR), pp. 1--8, 2008.
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B. Ni, S. Yan, and A. Kassim, "Recognizing human group activity with localized causalities," IEEE Conf. Computer Vision and Pattern Recogntion (CVPR), pp. 1470--1477, 2009.
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Z. Cheng, L. Qin, Q. Huang, S. Jiang, and Q. Tian, "Group activity recognition by Gaussian process estimation," Int'l Conf. Pattern Recognition (ICPR), pp. 3228--3231, 2010.
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Y. Chen, W. Lin, H. Li, H. Luo, Y. Tao, and D. Liu, "A new package-group-transmission-based algorithm for human activity recognition in videos," IEEE Visual Communications and Image Processing(VCIP), pp. 1--4, 2011.
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W. Lin, M.-T. Sun, R. Poovendran, and Z. Zhang, "Activity recognition using a combination of category components and local models for video surveillance," IEEE Trans. Circuits Syst. Video Technol., vol. 20, no. 8, pp. 1057--1067, 2008.
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Y. Fang, M. Sun, M. Kim, and K. Ramani, "Heat-mapping: a robust approach toward perceptually consistent mesh segmentation," IEEE Conf. Computer Vision and Pattern Recogntion (CVPR), pp. 2145--2152, 2011.
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Cited By

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  • (2019)Rethinking the Combined and Individual Orders of Derivative of States for Differential Recurrent Neural NetworksACM Transactions on Multimedia Computing, Communications, and Applications10.1145/333792815:3(1-21)Online publication date: 12-Sep-2019
  • (2019)Video‐based method for detecting potential risks among multiple electric bikesIET Intelligent Transport Systems10.1049/iet-its.2018.555913:9(1440-1446)Online publication date: 11-Jun-2019
  • (2018)BlockmapComputational & Mathematical Organization Theory10.1007/s10588-017-9252-624:2(149-168)Online publication date: 1-Jun-2018
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  1. A new heat-map-based algorithm for human group activity recognition

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    cover image ACM Conferences
    MM '12: Proceedings of the 20th ACM international conference on Multimedia
    October 2012
    1584 pages
    ISBN:9781450310895
    DOI:10.1145/2393347
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 29 October 2012

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    Author Tags

    1. heat map
    2. human group activity recognition
    3. surface fitting

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    MM '12
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    MM '12: ACM Multimedia Conference
    October 29 - November 2, 2012
    Nara, Japan

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    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    Cited By

    View all
    • (2019)Rethinking the Combined and Individual Orders of Derivative of States for Differential Recurrent Neural NetworksACM Transactions on Multimedia Computing, Communications, and Applications10.1145/333792815:3(1-21)Online publication date: 12-Sep-2019
    • (2019)Video‐based method for detecting potential risks among multiple electric bikesIET Intelligent Transport Systems10.1049/iet-its.2018.555913:9(1440-1446)Online publication date: 11-Jun-2019
    • (2018)BlockmapComputational & Mathematical Organization Theory10.1007/s10588-017-9252-624:2(149-168)Online publication date: 1-Jun-2018
    • (2017)Group Activity Recognition with Differential Recurrent Convolutional Neural Networks2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)10.1109/FG.2017.70(526-531)Online publication date: May-2017
    • (2015)Spatio-Temporal Triangular-Chain CRF for Activity RecognitionProceedings of the 23rd ACM international conference on Multimedia10.1145/2733373.2806304(1151-1154)Online publication date: 13-Oct-2015
    • (2014)Representing And Recognizing Motion TrajectoriesProceedings of the 22nd ACM international conference on Multimedia10.1145/2647868.2654955(1077-1080)Online publication date: 3-Nov-2014
    • (2014)Recognizing human group action by layered model with multiple cuesNeurocomputing10.1016/j.neucom.2014.01.019136(124-135)Online publication date: Jul-2014
    • (2013)Temporal encoded F-formation system for social interaction detectionProceedings of the 21st ACM international conference on Multimedia10.1145/2502081.2502096(937-946)Online publication date: 21-Oct-2013
    • (2013)A Heat-Map-Based Algorithm for Recognizing Group Activities in VideosIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2013.226978023:11(1980-1992)Online publication date: 1-Nov-2013

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