Tracking and data association
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Approach to Moving Object Detection in 2D and 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Human Motion in Structured Environments Using a Distributed-Camera System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Tracking with an Active Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
Concerning Bayesian Motion Segmentation, Model, Averaging, Matching and the Trifocal Tensor
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Ill-Posed Problems and Regularization Analysis in Early Vision
Ill-Posed Problems and Regularization Analysis in Early Vision
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Evaluation of global image thresholding for change detection
Pattern Recognition Letters
Bilateral learning for color-based tracking
Image and Vision Computing
Detection and segmentation of moving objects in complex scenes
Computer Vision and Image Understanding
Robust object segmentation using probability-based background extraction algorithm
GVE '07 Proceedings of the IASTED International Conference on Graphics and Visualization in Engineering
Multimedia Tools and Applications
Stereo vision image processing strategy for moving object detecting
SENSIG'09/VIS'09/MATERIALS'09 Proceedings of the 2nd WSEAS International Conference on Sensors, and Signals and Visualization, Imaging and Simulation and Materials Science
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
Background modeling using color, disparity, and motion information
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Background updating for visual surveillance
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Adaptive background generation for video object segmentation
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Background subtraction using low rank and group sparsity constraints
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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A new background subtraction method is proposed in this paper for the foreground detection from a non-stationary background. Usually, motion compensation is required when applying background subtraction to a non-stationary background. In practice, it is difficult to realize this to sufficient pixel accuracy. The problem is further complicated when the moving objects to be detected/tracked are small, since the pixel error in motion compensating the background will hide the small targets. A spatial distribution of Gaussians model is proposed to deal with moving object detection where the motion compensation is not exact but approximated. The distribution of each background pixel is temporally and spatially modeled. Based on this statistical model, a pixel in the current frame is classified as belonging to the foreground or background. In addition, a new background restoration and adaptation algorithm is developed for the non-stationary background over an extended period of time. Test cases involving a surveillance system to detect small moving objects (human and car) within a highly textured background and a pan-tilt human tracking system are demonstrated successfully.