Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Reducing the Computational Cost of Computing Approximated Median Strings
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time and accurate segmentation of moving objects in dynamic scene
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Probabilistic Correlation of Monitoring Data for Fault Detection in Complex Systems
DSN '06 Proceedings of the International Conference on Dependable Systems and Networks
Robust abandoned object detection using dual foregrounds
EURASIP Journal on Advances in Signal Processing
VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
Background Subtraction on Distributions
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Unsupervised scene analysis: A hidden Markov model approach
Computer Vision and Image Understanding
Kernel bandwidth estimation for nonparametric modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Real-Time Detection and Tracking for Augmented Reality on Mobile Phones
IEEE Transactions on Visualization and Computer Graphics
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Fast image motion segmentation for surveillance applications
Image and Vision Computing
hiCUDA: High-Level GPGPU Programming
IEEE Transactions on Parallel and Distributed Systems
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
Modeling complex scenes for accurate moving objects segmentation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
A temporal-spatial background modeling of dynamic scenes
Frontiers of Computer Science in China
Bayesian adaptive bandwidth kernel density estimation of irregular multivariate distributions
Computational Statistics & Data Analysis
Towards robust object detection: integrated background modeling based on spatio-temporal features
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Robust detection of moving objects in video sequences through rough set theory framework
Image and Vision Computing
Non-parametric statistical background modeling for efficient foreground region detection
Machine Vision and Applications
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Answering to the growing demand of machine vision applications for the latest generation of electronic devices endowed with camera platforms, several moving object detection strategies have been proposed in recent years. Among them, spatio-temporal based non-parametric methods have recently drawn the attention of many researchers. These methods, by combining a background model and a foreground model, achieve high-quality detections in sequences recorded with non-completely static cameras and in scenarios containing complex backgrounds. However, since they have very high memory and computational associated costs, they apply some simplifications in the background modeling process, therefore decreasing the quality of the modeling. Here, we propose a novel background modeling that is applicable to any spatio-temporal non-parametric moving object detection strategy. Through an efficient and robust method to dynamically estimate the bandwidth of the kernels used in the modeling, both the usability and the quality of previous approaches are improved. Furthermore, by adding a novel mechanism to selectively update the background model, the number of misdetections is significantly reduced, achieving an additional quality improvement. Empirical studies on a wide variety of video sequences demonstrate that the proposed background modeling significantly improves the quality of previous strategies while maintaining the computational requirements of the detection process.