Open AccessParticle filter framework for salient object detection in videos
Author(s) -
Muthuswamy Karthik,
Rajan Deepu
Publication year - 2015
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2013.0298
Subject(s) - artificial intelligence , computer vision , computer science , salient , hue , particle filter , segmentation , object detection , feature (linguistics) , pattern recognition (psychology) , optical flow , foreground detection , tracking (education) , filter (signal processing) , image (mathematics) , psychology , pedagogy , linguistics , philosophy
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.