Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Background subtraction based on logarithmic intensities
Pattern Recognition Letters
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Detected motion classification with a double-background and a neighborhood-based difference
Pattern Recognition Letters
Fast Lighting Independent Background Subtraction
VS '98 Proceedings of the 1998 IEEE Workshop on Visual Surveillance
Cast Shadow Removing in Foreground Segmentation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Detecting Salient Motion by Accumulating Directionally-Consistent Flow
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
What Can Projections of Flow Fields Tell Us About the Visual Motion
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Motion detection via change-point detection for cumulative histograms of ratio images
Pattern Recognition Letters
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
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
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Motion segmentation using Markov random field model for accurate moving object segmentation
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Distributed visual sensing for virtual top-view trajectory generation in football videos
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Automatic Segmentation of Non-rigid Objects in Image Sequences Using Spatiotemporal Information
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
A spatially distributed model for foreground segmentation
Image and Vision Computing
Vision-based human pose estimation for pervasive computing
AMC '09 Proceedings of the 2009 workshop on Ambient media computing
Multimedia Tools and Applications
Moving vehicles detection based on adaptive motion histogram
Digital Signal Processing
Stereo-based object segmentation combining spatio-temporal information
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
A novel histogram-based feature representation and its application in sport players classification
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Stochastic approximation for background modelling
Computer Vision and Image Understanding
New optical flow approach for motion segmentation based on gamma distribution
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Human activities recognition using depth images
Proceedings of the 21st ACM international conference on Multimedia
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In this paper, we address the problem of moving object segmentation using background subtraction. Solving this problem is very important for many applications: visual surveillance of both in outdoor and indoor environments, traffic control, behavior detection during sport activities, and so on. All these applications require as a first step, the detection of moving objects in the observed scene before applying any further technique for object recognition and activity identification. We propose a reliable foreground segmentation algorithm that combines temporal image analysis with a reference background image. We are especially careful of the core problem arising in the analysis of outdoor daylight scenes: continuous variations of lighting conditions that cause unexpected changes in intensities on the background reference image. In this paper, a new approach for background adaptation to changes in illumination is presented. All the pixels in the image, even those covered by foreground objects, are continuously updated in the background model. The experimental results demonstrate the effectiveness of the proposed algorithm when applied in different outdoor and indoor environments.