Stationary background generation: an alternative to the difference of two images
Pattern Recognition
Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Tracking with Bayesian Estimation of Dynamic Layer Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrated Region- and Pixel-based Approach to Background Modelling
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
Support for effective use of multiple video streams in security
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Minimizing Video Data Using Looping Background Detection Technique
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Patch-based background initialization in heavily cluttered video
IEEE Transactions on Image Processing
Background estimation using graph cuts and inpainting
Proceedings of Graphics Interface 2010
A background subtraction algorithm for detecting and tracking vehicles
Expert Systems with Applications: An International Journal
Background subtraction for automated multisensor surveillance: a comprehensive review
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
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Most of the automated video-surveillance applications are based on background (BG) subtraction techniques, that aim at distinguishing moving objects in a static scene. These strategies strongly depend on the BG model, that has to be initialized and updated. A good initialization is crucial for the successive processing. In this paper, we propose a novel method for BG initialization and recovery, that merges interesting ideas coming from the video inpainting and the generative modelling subfields. The method takes as input a video sequence, in which several objects move in front of a stationary BG. Then, a statistical representation of the BG is iteratively built, discarding automatically the moving objects. The method is based on the following hypotheses: (i) a portion of the BG, called sure BG, can be identified with high certainty by using only per-pixel reasoning and (ii) the remaining scene BG can be generated utilizing exemplars of the sure BG. The proposed algorithm is able to exploit these hypotheses in a principled and effective way.