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Parsing collective behaviors by hierarchical model with varying structure

Authors:

Cong Zhang,

Xiaokang Yang,

Jun Zhu,

Weiyao LinAuthors Info & Claims

MM '12: Proceedings of the 20th ACM international conference on Multimedia

Pages 1085 - 1088

https://doi.org/10.1145/2393347.2396389

Published: 29 October 2012 Publication History

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Abstract

Collective behaviors are usually composed of several groups. Considering the interactions among groups, this paper presents a novel framework to parse collective behaviors for video surveillance applications. We first propose a latent hierarchical model (LHM) with varying structure to represent the behavior with multiple groups. Furthermore, we also propose a multi-layer-based (MLB) inference method, where a sample-based heuristic search (SHS) is introduced to infer the group affiliation. And latent SVM is adopted to learn our model. With the proposed LHM, not only are the collective behaviors detected effectively, but also the group affiliation in the collective behaviors is figured out. Experiment results demonstrate the effectiveness of the proposed framework.

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Cited By

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Tang JShu XYan RZhang L(2019)Coherence Constrained Graph LSTM for Group Activity RecognitionIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2019.2928540(1-1)Online publication date: 2019
https://doi.org/10.1109/TPAMI.2019.2928540
Wu YYe YZhao CZhou XSmeaton ATian QBulterman DShen HMayer-Patel KYan S(2015)Coherent Motion Detection with Collective Density ClusteringProceedings of the 23rd ACM international conference on Multimedia10.1145/2733373.2806227(361-370)Online publication date: 13-Oct-2015
https://dl.acm.org/doi/10.1145/2733373.2806227
Zhang XHu HJiang FLi B(2015)Pedestrian detection based on hierarchical co-occurrence model for occlusion handlingNeurocomputing10.1016/j.neucom.2015.05.038168:C(861-870)Online publication date: 30-Nov-2015
https://dl.acm.org/doi/10.1016/j.neucom.2015.05.038

Index Terms

Parsing collective behaviors by hierarchical model with varying structure
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Published In

MM '12: Proceedings of the 20th ACM international conference on Multimedia

October 2012

1584 pages

ISBN:9781450310895

DOI:10.1145/2393347

General Chairs:
Noboru Babaguchi
Osaka University, Japan
,
Kiyoharu Aizawa
The University of Tokyo, Japan
,
John Smith
IBM, USA
,
Program Chairs:
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Thomas Plagemann
University of Oslo, Norway
,
Xian-Sheng Hua
Microsoft, USA
,
Rong Yan
Facebook, USA

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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Association for Computing Machinery

New York, NY, United States

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Published: 29 October 2012

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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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Tang JShu XYan RZhang L(2019)Coherence Constrained Graph LSTM for Group Activity RecognitionIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2019.2928540(1-1)Online publication date: 2019
https://doi.org/10.1109/TPAMI.2019.2928540
Wu YYe YZhao CZhou XSmeaton ATian QBulterman DShen HMayer-Patel KYan S(2015)Coherent Motion Detection with Collective Density ClusteringProceedings of the 23rd ACM international conference on Multimedia10.1145/2733373.2806227(361-370)Online publication date: 13-Oct-2015
https://dl.acm.org/doi/10.1145/2733373.2806227
Zhang XHu HJiang FLi B(2015)Pedestrian detection based on hierarchical co-occurrence model for occlusion handlingNeurocomputing10.1016/j.neucom.2015.05.038168:C(861-870)Online publication date: 30-Nov-2015
https://dl.acm.org/doi/10.1016/j.neucom.2015.05.038

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Accounting for patterns of collective behavior in crowd locomotor dynamics for realistic simulations

Scalable Learning of Collective Behavior

Algebraic Characterization of Collective Behaviors for Directed Signed Networks

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