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Video Saliency Detection Using Motion Distinctiveness and Uniform Contrast Measure
Volume 30, Issue 1 (2019), pp. 53–72
Pub. online: 1 January 2019      Type: Research Article      Open accessOpen Access
Received
1 May 2018
Accepted
1 October 2018
Published
1 January 2019

Abstract

Saliency detection has been deeply studied in the last few years and the number of the designed computational models is increasing. Starting from the assumption that spatial and temporal information of an input video frame can provide better saliency results than using each information alone, we propose a spatio-temporal saliency model for detecting salient objects in videos. First, spatial saliency is measured at patch-level by fusing local contrasts with spatial priors to label each patch as a foreground or a background one. Then, the newly proposed motion distinctiveness feature and gradient flow field measure are used to obtain the temporal saliency maps. Finally, spatial and temporal saliency maps are fused together into one final saliency map.
On the challenging SegTrack v2 and Fukuchi benchmark datasets we significantly outperform the state-of-the-art methods.

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Biographies

Kalboussi Rahma
rahma.kalboussi@gmail.com

R. Kalboussi received her bachelor and engineer degree from the Higher Institute Higher Institute of Computer Science and Communication Techniques. She is currently a PhD candidate in computer sciences. She is also a researcher at the Networked Objects, Control and Communication Systems (NOCCS) Research Laboratory, National School of Engineering of Sousse, Tunisia. Her research interests include image and video processing, pattern recognition, computer vision and machine learning.

Abdellaoui Mehrez

M. Abdellaoui received his engineer, MS and PhD degrees from the National School of Engineering of Monastir, Tunisia, in 2003, 2005 and 2012, respectively. He is currently an assistant professor in Signal and Image Processing at the High Institute of Applied Technologies, University of Kairouan. He is also a researcher at the Networked Objects, Control and Communication Systems (NOCCS) Research Laboratory, National School of Engineering of Sousse, Tunisia. His research interests include image and video processing, computer vision and machine learning.

Douik Ali

A. Douik received his MSEE degree in 1990 and PhD degree in 1996, both from ENSET, University of Tunis. He is currently a full professor in signal and image processing at the National Engineering School of Sousse. He is also a researcher at the Networked Objects, Control and Communication Systems (NOCCS) Research Laboratory, National School of Engineering of Sousse, Tunisia. His research interests include image and video processing, machine learning and control.


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© 2019 Vilnius University
Open access article under the CC BY license.

Keywords
saliency detection motion estimation object of interest optical flow contrast measure

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