Open AccessRobust moving object detection using compressed sensing
Author(s) -
Kang Bin,
Zhu WeiPing
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2015.0103
Subject(s) - artificial intelligence , computer vision , computer science , object detection , object (grammar) , viola–jones object detection framework , object class detection , motion detection , noise (video) , compressed sensing , video tracking , video denoising , pattern recognition (psychology) , motion (physics) , image (mathematics) , face detection , multiview video coding , facial recognition system
Moving object detection plays a key role in video surveillance. A number of object detection methods have been proposed in the spatial domain. In this study, the authors propose a compressed sensing‐based algorithm for the detection of moving object. They first use a practical three‐dimensional circulant sampling method to yield sampled measurements. Then, they propose an object detection model to simultaneously reconstruct the foreground support, background and video sequence using the sampled measurements directly. Experimental results show that the proposed moving object detection algorithm outperforms the state‐of‐the‐art approaches and it is robust to the movement turbulence, camera motion and video noise.