C4.5: programs for machine learning
C4.5: programs for machine learning
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
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
Frame-Rate Omnidirectional Surveillance & Tracking of Camouflaged and Occluded Targets
VS '99 Proceedings of the Second IEEE Workshop on Visual Surveillance
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Moving Shadow and Object Detection in Traffic Scenes
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Efficient nonparametric kernel density estimation for real time computer vision
Efficient nonparametric kernel density estimation for real time computer vision
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Real-time and accurate segmentation of moving objects in dynamic scene
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
On the Spatial Statistics of Optical Flow
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
WebGuard: A Web Filtering Engine Combining Textual, Structural, and Visual Content-Based Analysis
IEEE Transactions on Knowledge and Data Engineering
Background Subtraction and Shadow Detection in Grayscale Video Sequences
SIBGRAPI '05 Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing
Salient Motion Information Detection Technique Using Weighted Subtraction Image and Motion Vector
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01
Robust foreground segmentation based on two effective background models
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Independent component analysis-based background subtraction for indoor surveillance
IEEE Transactions on Image Processing
Accurate Background Modeling for Moving Object Detection in a Dynamic Scene
DICTA '10 Proceedings of the 2010 International Conference on Digital Image Computing: Techniques and Applications
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
Advances in background updating and shadow removing for motion detection algorithms
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
Optical flow estimation using temporally oversampled video
IEEE Transactions on Image Processing
Cast shadow detection based on semi-supervised learning
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
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Fast and accurate moving object segmentation in dynamic scenes is the first step in many computer vision applications. In this paper, we propose a new background modeling method for moving object segmentation based on dynamic matrix and spatio-temporal analyses of scenes. Our method copes with some challenges related to this field. A new algorithm is proposed to detect and remove cast shadow. A comparative study by quantitative evaluations shows that the proposed approach can detect foreground robustly and accurately from videos recorded by a static camera and which include several constraints. A Highway Control and Management System called RoadGuard is proposed to show the robustness of our method. In fact, our system has the ability to control highway by detecting strange events that can happen like vehicles suddenly stopped in roads, parked vehicles in emergency zones or even illegal conduct such as going out from the road. Moreover, RoadGuard is capable of managing highways by saving information about the date and time of overloaded roads.