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Article

Distilling information with super-resolution for video surveillance

Authors:

Marco Cristani,

Dong Seon Cheng,

Vittorio Murino,

Donato PannulloAuthors Info & Claims

VSSN '04: Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks

Pages 2 - 11

https://doi.org/10.1145/1026799.1026803

Published: 15 October 2004 Publication History

Abstract

A video surveillance sequence generally contains a lot of scattered information regarding several objects in cluttered scenes. Especially in case of use of digital hand-held cameras, the overall quality is very low due to the unstable motion and the low resolution, even if multiple shots of the desired target are available.

To overcome these limitations, we propose a novel Bayesian framework based on image superresolution, that integrates all the informative bits of a target and condenses the redundancy. We call this process <i>distillation</i>.

In the traditional formulation of the image super-resolution problem, the observed target is (1) always the same, (2) acquired using a camera making small movements, and (3) the number of available images is sufficient for recovering high frequency information. These hypotheses obviously do not hold in the concrete situations described above.

In this paper, we extend and generalize the image super-resolution task, embedding it in a structured framework that accurately distills the necessary information. In short, ourapproach is composed by two phases. First, a transformation-invariant video clustering coarsely groups and registers the frames, also defining a similarity concept among them. Second, a novel Bayesian super-resolution method uses this concept in order to combine selectively all the pixels of similar frames, whose result consists in a highly informative super-resolved image of the desired target.

Our approach is first tested on synthetic data, obtaining encouraging comparative results with respect to known super-resolution techniques and a definite robustness against noise. Second, real data coming from videos taken by a hand-held camera are considered, trying to solve the major details of a person in motion, a typical setting of video surveillance applications.

References

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Cited By

Du JWang MWang XYang ZLi XWu X(2024)Reference-based image super-resolution with attention extraction and pooling of residualsThe Journal of Supercomputing10.1007/s11227-024-06587-881:1Online publication date: 4-Dec-2024
https://doi.org/10.1007/s11227-024-06587-8
Mehta NMurala S(2023)(MLE$^{2}$A$^{2}$U)-Net: Image Super-Resolution via Multi-Level Edge Embedding and Aggregated Attentive Upsampler NetworkIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2022.31826547:2(523-536)Online publication date: Apr-2023
https://doi.org/10.1109/TETCI.2022.3182654
Chen JYe YKang M(2020)Image Super-Resolution Using Hybrid Attention MechanismProceedings of the 2020 4th International Conference on Video and Image Processing10.1145/3447450.3447460(62-67)Online publication date: 25-Dec-2020
https://dl.acm.org/doi/10.1145/3447450.3447460
Show More Cited By

Index Terms

Distilling information with super-resolution for video surveillance
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Video summarization
  2. Computer graphics
    1. Image manipulation

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Information

Published In

cover image ACM Conferences

VSSN '04: Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks

October 2004

152 pages

ISBN:1581139349

DOI:10.1145/1026799

Conference Chairs:
Edward Chang
University of California, Santa Barbara
,
Yuan-Fang Wang
University of California, Santa Barbara
,
Feng Zhao
Microsoft Research

Copyright © 2004 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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Publication History

Published: 15 October 2004

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MM04: 2004 12th Annual ACM International Conference on Multimedia

October 15, 2004

NY, New York, USA

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Cited By

Du JWang MWang XYang ZLi XWu X(2024)Reference-based image super-resolution with attention extraction and pooling of residualsThe Journal of Supercomputing10.1007/s11227-024-06587-881:1Online publication date: 4-Dec-2024
https://doi.org/10.1007/s11227-024-06587-8
Mehta NMurala S(2023)(MLE$^{2}$A$^{2}$U)-Net: Image Super-Resolution via Multi-Level Edge Embedding and Aggregated Attentive Upsampler NetworkIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2022.31826547:2(523-536)Online publication date: Apr-2023
https://doi.org/10.1109/TETCI.2022.3182654
Chen JYe YKang M(2020)Image Super-Resolution Using Hybrid Attention MechanismProceedings of the 2020 4th International Conference on Video and Image Processing10.1145/3447450.3447460(62-67)Online publication date: 25-Dec-2020
https://dl.acm.org/doi/10.1145/3447450.3447460
Sang LHaefner BCremers D(2020)Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach2020 IEEE Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV45572.2020.9093491(1-10)Online publication date: Mar-2020
https://doi.org/10.1109/WACV45572.2020.9093491
Islam FLin JArchibald RAbernathy DAl-Qasir ICampbell AStone MGranroth G(2019)Super-resolution energy spectra from neutron direct-geometry spectrometersReview of Scientific Instruments10.1063/1.511614790:10(105109)Online publication date: 1-Oct-2019
https://doi.org/10.1063/1.5116147
Marenzi ETorti ELeporati FQuevedo ECallicò G(2017)Block matching super-resolution parallel GPU implementation for computational imagingIEEE Transactions on Consumer Electronics10.1109/TCE.2017.01507763:4(368-376)Online publication date: Nov-2017
https://doi.org/10.1109/TCE.2017.015077
Marenzi ECarrus ADanese GLeporati FCallico G(2017)Efficient Parallelization of Motion Estimation for Super-Resolution2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)10.1109/PDP.2017.64(274-277)Online publication date: 2017
https://doi.org/10.1109/PDP.2017.64
Peng SHaefner BQueau YCremers D(2017)Depth Super-Resolution Meets Uncalibrated Photometric Stereo2017 IEEE International Conference on Computer Vision Workshops (ICCVW)10.1109/ICCVW.2017.349(2961-2968)Online publication date: Oct-2017
https://doi.org/10.1109/ICCVW.2017.349
Rasti PNasrollahi KOrlova OTamberg GOzcinar CMoeslund TAnbarjafari G(2017)A new low‐complexity patch‐based image super‐resolutionIET Computer Vision10.1049/iet-cvi.2016.046311:7(567-576)Online publication date: 30-Aug-2017
https://dl.acm.org/doi/10.1049/iet-cvi.2016.0463
Bellantonio MHaque MRodriguez PNasrollahi KTelve TEscalera SGonzalez JMoeslund TRasti PAnbarjafari G(2017)Spatio-temporal Pain Recognition in CNN-Based Super-Resolved Facial ImagesVideo Analytics. Face and Facial Expression Recognition and Audience Measurement10.1007/978-3-319-56687-0_13(151-162)Online publication date: 29-Mar-2017
https://doi.org/10.1007/978-3-319-56687-0_13
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