Particle filter framework for salient object detection in videos
- Author(s): Karthik Muthuswamy 1 and Deepu Rajan 1
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View affiliations
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Affiliations:
1:
Centre for Multimedia and Network Technology, School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, N4-02C-92 639798, Singapore
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Affiliations:
1:
Centre for Multimedia and Network Technology, School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, N4-02C-92 639798, Singapore
- Source:
Volume 9, Issue 3,
June 2015,
p.
428 – 438
DOI: 10.1049/iet-cvi.2013.0298 , Print ISSN 1751-9632, Online ISSN 1751-9640
Salient object detection in videos is challenging because of the competing motion in the background, resulting from camera tracking an object of interest, or motion of objects in the foreground. The authors present a fast method to detect salient video objects using particle filters, which are guided by spatio-temporal saliency maps and colour feature with the ability to quickly recover from false detections. The proposed method for generating spatial and motion saliency maps is based on comparing local features with dominant features present in the frame. A region is marked salient if there is a large difference between local and dominant features. For spatial saliency, hue and saturation features are used, while for motion saliency, optical flow vectors are used as features. Experimental results on standard datasets for video segmentation and for saliency detection show superior performance over state-of-the-art methods.
Inspec keywords: image sequences; image segmentation; video signal processing; particle filtering (numerical methods); object detection; image colour analysis
Other keywords: object of interest; video segmentation; salient video object detection; spatiotemporal saliency maps; saturation features; local features; colour feature; optical flow vectors; spatial saliency maps; particle filter framework; motion saliency maps; hue features; dominant features; standard datasets
Subjects: Filtering methods in signal processing; Video signal processing; Computer vision and image processing techniques; Optical, image and video signal processing
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