NDLI: Particle filter framework for salient object detection in videos

Content Provider	IET Digital Library
Author	Muthuswamy, Karthik Rajan, Deepu
Abstract	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.
Starting Page	428
Ending Page	438
Page Count	11
ISSN	17519632
Volume Number	9
e-ISSN	17519640
Issue Number	Issue 3, Jun (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/9/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2013.0298
Journal	IET Computer Vision
Publisher Date	2015-02-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Colour Feature Computer Vision And Image Processing Technique Dominant Features Filtering Method in Signal Processing Hue Feature Image Colour Analysis Image Segmentation Image Sequence Local Feature Motion Saliency Maps Object Detection Object of Interest Optical Flow Vector Optical, Image And Video Signal Processing Particle Filter Particle Filter Framework Salient Video Object Detection Saturation Feature Spatial Saliency Maps Spatiotemporal Saliency Maps Standard Datasets Video Segmentation Video Signal Processing
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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