Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Compact Representations of Videos Through Dominant and Multiple Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual organization based computational model for robust segmentation of moving objects
Computer Vision and Image Understanding
Statistical background modeling for non-stationary camera
Pattern Recognition Letters
Real-Time Fixation, Mosaic Construction and Moving Object Detection from a Moving Camera
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Evaluation of global image thresholding for change detection
Pattern Recognition Letters
Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shadow Detection by Combined Photometric Invariants for Improved Foreground Segmentation
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Bi-Layer Segmentation of Binocular Stereo Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Robust Moving Object Detection at Distance in the Visible Spectrum and Beyond Using A Moving Camera
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Segmentation and tracking of multiple video objects
Pattern Recognition
Map-Enhanced Detection and Tracking from a Moving Platform with Local and Global Data Association
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Monocular Video Foreground/Background Segmentation by Tracking Spatial-Color Gaussian Mixture Models
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Panoramic Background Model under Free Moving Camera
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Video Objects Segmentation by Robust Background Modeling
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Robust Foreground Detection In Video Using Pixel Layers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and accurate global motion estimation algorithm using pixel subsampling
Information Sciences: an International Journal
Scene modeling and change detection in dynamic scenes: A subspace approach
Computer Vision and Image Understanding
An efficient image-mosaicing method based on multifeature matching
Machine Vision and Applications
Moving object segmentation by background subtraction and temporal analysis
Image and Vision Computing
Markovian framework for foreground-background-shadow separation of real world video scenes
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Automatic video object tracking using a mosaic-based background
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image matting through a Web browser
Multimedia Tools and Applications
Issues and contributions in interactive multimedia: photos, mobile multimedia, and interactive TV
Multimedia Tools and Applications
Hi-index | 0.00 |
This paper explores a robust region-based general framework for discriminating between background and foreground objects within a complex video sequence. The proposed framework works under difficult conditions such as dynamic background and nominally moving camera. The originality of this work lies essentially in our use of the semantic information provided by the regions while simultaneously identifying novel objects (foreground) and non-novel ones (background). The information of background regions is exploited to make moving objects detection more efficient, and vice-versa. In fact, an initial panoramic background is modeled using region-based mosaicing in order to be sufficiently robust to noise from lighting effects and shadowing by foreground objects. After the elimination of the camera movement using motion compensation, the resulting panoramic image should essentially contain the background and the ghost-like traces of the moving objects. Then, while comparing the panoramic image of the background with the individual frames, a simple median-based background subtraction permits a rough identification of foreground objects. Joint background-foreground validation, based on region segmentation, is then used for a further examination of individual foreground pixels intended to eliminate false positives and to localize shadow effects. Thus, we first obtain a foreground mask from a slow-adapting algorithm, and then validate foreground pixels (moving visual objects + shadows) by a simple moving object model built by using both background and foreground regions. The tests realized on various well-known challenging real videos (across a variety of domains) show clearly the robustness of the suggested solution. This solution, which is relatively computationally inexpensive, can be used under difficult conditions such as dynamic background, nominally moving camera and shadows. In addition to the visual evaluation, spatial-based evaluation statistics, given hand-labeled ground truth, has been used as a performance measure of moving visual objects detection.