short-paper

Human Action Recognition With Trajectory Based Covariance Descriptor In Unconstrained Videos

Authors:

Jun WuAuthors Info & Claims

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

Pages 1175 - 1178

https://doi.org/10.1145/2733373.2806310

Published: 13 October 2015 Publication History

Abstract

Human action recognition from realistic videos plays a key role in multimedia event detection and understanding. In this paper, a novel Trajectory Based Covariance (TBC) descriptor is proposed, which is formulated along the dense trajectories. To map the descriptor matrix to vector space and trim out the redundancy of data, the TBC descriptor matrix is projected to Euclidean space by the Logarithm Principal Components Analysis (LogPCA). Our method is tested on the challenging Hollywood2 and TV Human Interaction datasets. Experimental results show that the proposed TBC descriptor outperforms three baseline descriptors (i.e., histogram of oriented gradient, histogram of optical flow and motion boundary histogram), and our method achieves better recognition performances than a number of state-of-the-art approaches.

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Cited By

Kushwaha AKhare APrakash O(2023)Micro-network-based deep convolutional neural network for human activity recognition from realistic and multi-view visual dataNeural Computing and Applications10.1007/s00521-023-08440-035:18(13321-13341)Online publication date: 13-Mar-2023
https://doi.org/10.1007/s00521-023-08440-0
Dong SHu DLi RGe M(2019)Human Action Recognition Based on Foreground Trajectory and Motion Difference DescriptorsApplied Sciences10.3390/app91021269:10(2126)Online publication date: 24-May-2019
https://doi.org/10.3390/app9102126
Yi YWang H(2019)Multi-modal learning for affective content analysis in moviesMultimedia Tools and Applications10.1007/s11042-018-5662-978:10(13331-13350)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s11042-018-5662-9
Show More Cited By

Index Terms

Human Action Recognition With Trajectory Based Covariance Descriptor In Unconstrained Videos
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding

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Information

Published In

cover image ACM Conferences

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

October 2015

1402 pages

ISBN:9781450334594

DOI:10.1145/2733373

General Chairs:
Xiaofang Zhou
The University of Queensland, Australia
,
Alan F. Smeaton
Dublin City University, Ireland
,
Qi Tian
The University of Texas at San Antonio, USA
,
Program Chairs:
Dick C.A. Bulterman
FXPAL, USA
,
Heng Tao Shen
The University of Queensland, Australia
,
Ketan Mayer-Patel
The University of North Carolina, USA
,
Shuicheng Yan
National University of Singapore, Singapore

Copyright © 2015 ACM.

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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2015

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Short-paper

Funding Sources

Fundamental Research Funds for the Central Universities
``Shu Guang' project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation
Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning
National Natural Science Foundation of China

Conference

MM '15

Sponsor:

SIGMM

MM '15: ACM Multimedia Conference

October 26 - 30, 2015

Brisbane, Australia

Acceptance Rates

MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;

Overall Acceptance Rate 995 of 4,171 submissions, 24%

Upcoming Conference

MM '24

Sponsor:
sigmm

The 32nd ACM International Conference on Multimedia

October 28 - November 1, 2024

Melbourne , VIC , Australia

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Cited By

Kushwaha AKhare APrakash O(2023)Micro-network-based deep convolutional neural network for human activity recognition from realistic and multi-view visual dataNeural Computing and Applications10.1007/s00521-023-08440-035:18(13321-13341)Online publication date: 13-Mar-2023
https://doi.org/10.1007/s00521-023-08440-0
Dong SHu DLi RGe M(2019)Human Action Recognition Based on Foreground Trajectory and Motion Difference DescriptorsApplied Sciences10.3390/app91021269:10(2126)Online publication date: 24-May-2019
https://doi.org/10.3390/app9102126
Yi YWang H(2019)Multi-modal learning for affective content analysis in moviesMultimedia Tools and Applications10.1007/s11042-018-5662-978:10(13331-13350)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s11042-018-5662-9
Yi YWang H(2018)Motion keypoint trajectory and covariance descriptor for human action recognitionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-016-1345-634:3(391-403)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s00371-016-1345-6
Li JWong YZhao QKankanhalli MLiu QLienhart RWang HChen SBoll SChen PFriedland GLi JYan S(2017)Attention Transfer from Web Images for Video RecognitionProceedings of the 25th ACM international conference on Multimedia10.1145/3123266.3123432(1-9)Online publication date: 23-Oct-2017
https://dl.acm.org/doi/10.1145/3123266.3123432
Yi YWang HZhang B(2017)Learning correlations for human action recognition in videosMultimedia Tools and Applications10.1007/s11042-017-4416-476:18(18891-18913)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s11042-017-4416-4
Zhang BWang LWang ZQiao YWang H(2016)Real-Time Action Recognition with Enhanced Motion Vector CNNs2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2016.297(2718-2726)Online publication date: Jun-2016
https://doi.org/10.1109/CVPR.2016.297
Wang HZheng XXiao B(2016)Accelerating Large-Scale Human Action Recognition with GPU-Based SparkAdvances in Multimedia Information Processing - PCM 201610.1007/978-3-319-48896-7_66(670-679)Online publication date: 27-Nov-2016
https://doi.org/10.1007/978-3-319-48896-7_66
Ergezer HLeblebicioğlu K(2016)Anomaly Detection and Activity Perception Using Covariance Descriptor for TrajectoriesComputer Vision – ECCV 2016 Workshops10.1007/978-3-319-48881-3_51(728-742)Online publication date: 3-Nov-2016
https://doi.org/10.1007/978-3-319-48881-3_51

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