Summarisation of short-term and long-term videos using texture and colour

Carvajal, Johanna, McCool, Chris and Sanderson, Conrad (2014). Summarisation of short-term and long-term videos using texture and colour. In: 2014 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE Winter Conference on Applications of Computer Vision (WACV 2014), Steamboat Springs, CO, United States, (769-775). 24-26 March 2014. doi:10.1109/WACV.2014.6836025

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Author Carvajal, Johanna
McCool, Chris
Sanderson, Conrad
Title of paper Summarisation of short-term and long-term videos using texture and colour
Conference name IEEE Winter Conference on Applications of Computer Vision (WACV 2014)
Conference location Steamboat Springs, CO, United States
Conference dates 24-26 March 2014
Convener IEEE
Proceedings title 2014 IEEE Winter Conference on Applications of Computer Vision (WACV)   Check publisher's open access policy
Journal name 2014 Ieee Winter Conference On Applications of Computer Vision (Wacv)   Check publisher's open access policy
Series IEEE Workshop on Applications of Computer Vision. Proceedings
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2014
Year available 2014
Sub-type Fully published paper
DOI 10.1109/WACV.2014.6836025
Open Access Status
ISBN 9781479949854
9781479949847
ISSN 1550-5790
Start page 769
End page 775
Total pages 7
Collection year 2015
Language eng
Abstract/Summary We present a novel approach to video summarisation that makes use of a Bag-of-visual-Textures (BoT) approach. Two systems are proposed, one based solely on the BoT approach and another which exploits both colour information and BoT features. On 50 short-term videos from the Open Video Project we show that our BoT and fusion systems both achieve state-of-the-art performance, obtaining an average F-measure of 0.83 and 0.86 respectively, a relative improvement of 9% and 13% when compared to the previous state-of-the-art. When applied to a new underwater surveillance dataset containing 33 long-term videos, the proposed system reduces the amount of footage by a factor of 27, with only minor degradation in the information content. This order of magnitude reduction in video data represents significant savings in terms of time and potential labour cost when manually reviewing such footage.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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