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Critical video quality for distributed automated video surveillance

Published: 06 November 2005 Publication History

Abstract

Large-scale distributed video surveillance systems pose new scalability challenges. Due to the large number of video sources in such systems, the amount of bandwidth required to transmit video streams for monitoring often strains the capability of the network. On the other hand, large-scale surveillance systems often rely on computer vision algorithms to automate surveillance tasks. We observe that these surveillance tasks present an opportunity for trade-off between the accuracy of the tasks and the bit rate of the video being sent. This paper shows that there exists a sweet spot, which we term critical video quality that can be used to reduce video bit rate without significantly affecting the accuracy of the surveillance tasks. We demonstrate this point by running extensive experiments on standard face detection and face tracking algorithms. Our experiments show that face detection works equally well even if the quality of compression is significantly reduced, and face tracking still works even if the frame rate is reduced to 6 frames per second. We further develop a prototype video surveillance system to demonstrate this idea. Our evaluation shows that we can achieve up to 29 times reduction in video bit rate when detecting faces and 16 times reduction when tracking faces. This paper also proposes a formal rate-accuracy optimization framework which can be used to determine appropriate encoding parameters in distributed video surveillance systems that are subjected to either bandwidth constraints or accuracy constraints.

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cover image ACM Conferences
MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia
November 2005
1110 pages
ISBN:1595930442
DOI:10.1145/1101149
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: 06 November 2005

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Author Tags

  1. rate-accuracy function
  2. video quality adaptation
  3. video surveillance

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MM05

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MULTIMEDIA '05 Paper Acceptance Rate 49 of 312 submissions, 16%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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  • (2019)How to Assess the Quality of Compressed Surveillance Videos Using Face RecognitionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2018.286670129:8(2229-2243)Online publication date: Aug-2019
  • (2019)Assessing the Impact of Video Compression on Background SubtractionPattern Recognition10.1007/978-3-030-41404-7_8(105-118)Online publication date: 26-Nov-2019
  • (2018)Scalable distributed visual computing for line-rate video streamsProceedings of the 9th ACM Multimedia Systems Conference10.1145/3204949.3204974(186-194)Online publication date: 12-Jun-2018
  • (2018)STRATEGIES FOR QUALITY-AWARE VIDEO CONTENT ANALYTICS2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)10.1109/SSIAI.2018.8470354(77-80)Online publication date: Apr-2018
  • (2018)Determining the Necessary Frame Rate of Video Data for Object Tracking under Accuracy Constraints2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)10.1109/MIPR.2018.00081(368-371)Online publication date: Apr-2018
  • (2017)Surveillance Video Quality Assessment Based on Face RecognitionProceedings of the on Thematic Workshops of ACM Multimedia 201710.1145/3126686.3130239(520-528)Online publication date: 23-Oct-2017
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