Journal ArticleDOI
Image change detection algorithms: a systematic survey
TLDR
In this paper, the authors present a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling.Abstract:
Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.read more
Citations
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Journal ArticleDOI
Exploring Contextual Redundancy in Improving Object-Based Video Coding for Video Sensor Networks Surveillance
Tsung-Han Tsai,Chung-Yuan Lin +1 more
TL;DR: An improved object-based coding architecture, namely dual-closed-loop encoder, is derived and it encodes the classified context of MB in an operational rate-distortion-optimized sense, showing that the proposed coding framework can achieve higher coding efficiency than MPEG-4 coding and related object- based coding approaches, while significantly reducing coding complexity.
Proceedings ArticleDOI
Real-time background subtraction for video surveillance: From research to reality
TL;DR: This paper reviews and evaluates performance of few common background subtraction algorithms which are medianbased, Gaussian-based and Kernel density-based approaches, testing their performance using four sets of image sequences contributed by Wallflower datasets.
Proceedings ArticleDOI
Statistical Background Modeling: An Edge Segment based Moving Object Detection Approach
TL;DR: An edge segment based statistical background modelling algorithm and a moving edge detection framework for the detection of moving objects that makes efficient use of statistical background model using the edge-segment structure are proposed.
Journal ArticleDOI
Fast unsupervised deep fusion network for change detection of multitemporal SAR images
TL;DR: A fast unsupervised deep fusion framework for change detection of multitemporal synthetic aperture radar (SAR) images is presented and designs a fusion network structure that can combine ratio operator based method to ensure that the representations of higher layers are better than that of the lower ones.
Proceedings ArticleDOI
Towards Detecting Changes in Underwater Image Sequences
TL;DR: The developed method was able to correctly find the changes between two challenging sequences of images from a coral reef taken one year apart and acquired with two different cameras.
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