Stationary background generation: an alternative to the difference of two images
Pattern Recognition
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Hidden Markov Model-Based Approach to Sequential Data Clustering
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A sequential pruning strategy for the selection of the number of states in hidden Markov models
Pattern Recognition Letters
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Integrated Region- and Pixel-based Approach to Background Modelling
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Similarity-based clustering of sequences using hidden Markov models
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Unsupervised scene analysis: a hidden Markov model approach
Computer Vision and Image Understanding
Unsupervised scene analysis: A hidden Markov model approach
Computer Vision and Image Understanding
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
A novel highway background generate approach
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
A novel robust statistical method for background initialization and visual surveillance
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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Most of the automated video-surveillance applications are based on the process of background modelling, aimed at discriminating motion patterns of interest at pixel, region or frame level in a nearly static scene. The issues characterizing an ordinary background modelling process are typically three: the background model representation, the initialization, and the adaptation. This paper proposes a novel initialization algorithm, able to bootstrap an integrated pixel and region-based background modelling algorithm. The input is an uncontrolled video sequence in which moving objects are present, the output is a pixel- and region-level statistical background model describing the static information of a scene. At the pixel level, multiple hypotheses of the background values are generated by modelling the intensity of each pixel with a Hidden Markov Model (HMM), also capturing the sequentiality of the different color (or gray-level) intensities. At the region level, the resulting HMMs are clustered with a novel similarity measure, able to remove moving objects from a sequence, and obtaining a segmented image of the observed scene, in which each region is characterized by a similar spatio-temporal evolution. Experimental trials on synthetic and real sequences have shown the effectiveness of the proposed approach.