Quantization optimized H.264 encoding for traffic video tracking applications

E. Soyak; S. A. Tsaftaris; Aggelos K Katsaggelos

doi:10.1109/ICIP.2010.5652364

Quantization optimized H.264 encoding for traffic video tracking applications

E. Soyak^*, S. A. Tsaftaris, Aggelos K Katsaggelos

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

The compression of video can reduce the accuracy of post-compression tracking algorithms. This is problematic for centralized applications such as traffic surveillance systems, where remotely captured and compressed video is transmitted to a central location for tracking. We propose a low complexity optimization framework that automatically identifies video features critical to tracking and concentrates bitrate on these features via quantization tables. Using the H.264 video coding standard and two commonly used state-of-the-art trackers we show that our algorithm allows for over 60% bitrate savings while maintaining comparable tracking accuracy.

Original language	English (US)
Title of host publication	2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages	1241-1244
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2010.5652364
State	Published - Dec 1 2010
Event	2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong Duration: Sep 26 2010 → Sep 29 2010

Other

Other	2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/Territory	Hong Kong
City	Hong Kong
Period	9/26/10 → 9/29/10

Keywords

Optimal quantization
Urban traffic video tracking
Video compression

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/ICIP.2010.5652364

Cite this

@inproceedings{2985b702177d438385deac41ece20382,

title = "Quantization optimized H.264 encoding for traffic video tracking applications",

abstract = "The compression of video can reduce the accuracy of post-compression tracking algorithms. This is problematic for centralized applications such as traffic surveillance systems, where remotely captured and compressed video is transmitted to a central location for tracking. We propose a low complexity optimization framework that automatically identifies video features critical to tracking and concentrates bitrate on these features via quantization tables. Using the H.264 video coding standard and two commonly used state-of-the-art trackers we show that our algorithm allows for over 60% bitrate savings while maintaining comparable tracking accuracy.",

keywords = "Optimal quantization, Urban traffic video tracking, Video compression",

author = "E. Soyak and Tsaftaris, {S. A.} and Katsaggelos, {Aggelos K}",

year = "2010",

month = dec,

day = "1",

doi = "10.1109/ICIP.2010.5652364",

language = "English (US)",

isbn = "9781424479948",

pages = "1241--1244",

booktitle = "2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings",

note = "2010 17th IEEE International Conference on Image Processing, ICIP 2010 ; Conference date: 26-09-2010 Through 29-09-2010",

}

Soyak, E, Tsaftaris, SA & Katsaggelos, AK 2010, Quantization optimized H.264 encoding for traffic video tracking applications. in 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings., 5652364, pp. 1241-1244, 2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, Hong Kong, 9/26/10. https://doi.org/10.1109/ICIP.2010.5652364

TY - GEN

T1 - Quantization optimized H.264 encoding for traffic video tracking applications

AU - Soyak, E.

AU - Tsaftaris, S. A.

AU - Katsaggelos, Aggelos K

PY - 2010/12/1

Y1 - 2010/12/1

N2 - The compression of video can reduce the accuracy of post-compression tracking algorithms. This is problematic for centralized applications such as traffic surveillance systems, where remotely captured and compressed video is transmitted to a central location for tracking. We propose a low complexity optimization framework that automatically identifies video features critical to tracking and concentrates bitrate on these features via quantization tables. Using the H.264 video coding standard and two commonly used state-of-the-art trackers we show that our algorithm allows for over 60% bitrate savings while maintaining comparable tracking accuracy.

AB - The compression of video can reduce the accuracy of post-compression tracking algorithms. This is problematic for centralized applications such as traffic surveillance systems, where remotely captured and compressed video is transmitted to a central location for tracking. We propose a low complexity optimization framework that automatically identifies video features critical to tracking and concentrates bitrate on these features via quantization tables. Using the H.264 video coding standard and two commonly used state-of-the-art trackers we show that our algorithm allows for over 60% bitrate savings while maintaining comparable tracking accuracy.

KW - Optimal quantization

KW - Urban traffic video tracking

KW - Video compression

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DO - 10.1109/ICIP.2010.5652364

M3 - Conference contribution

AN - SCOPUS:78651080376

SN - 9781424479948

SP - 1241

EP - 1244

BT - 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings

T2 - 2010 17th IEEE International Conference on Image Processing, ICIP 2010

Y2 - 26 September 2010 through 29 September 2010

ER -

Quantization optimized H.264 encoding for traffic video tracking applications

Abstract

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Keywords

ASJC Scopus subject areas

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