Frame-Rate Omnidirectional Surveillance & Tracking of Camouflaged and Occluded Targets
VS '99 Proceedings of the Second IEEE Workshop on Visual Surveillance
Statistical Background Subtraction for a Mobile Observer
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust and Efficient Foreground Analysis for Real-Time Video Surveillance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Background Generation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
CRV '07 Proceedings of the Fourth Canadian Conference on Computer and Robot Vision
Robust Foreground Segmentation Using Subspace Based Background Model
APCIP '09 Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Accurate Foreground Segmentation without Pre-learning
ICIG '11 Proceedings of the 2011 Sixth International Conference on Image and Graphics
AVSS '11 Proceedings of the 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance
IEEE Transactions on Image Processing
Background subtraction with dirichlet processes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
| Hi-index | 0.01 |
Background modeling and foreground segmentation are the foundation of traffic surveillance systems. The preciseness of the background model and the accuracy of the foreground segmentation will directly affect the subsequent operations, such as object detection, target classification and behavior understanding. Additionally, the processing time is limited for real applications. The background modeling and foreground segmentation approaches, unfortunately, often have to make two tough trade-offs, including the one between the robustness to background changes and the sensitivity to foreground abnormalities and the other between suppressing noise and reducing the erroneous holes and splitting in foreground segmentation. To deal with these problems, an improved background modeling and foreground segmentation approach based on the feedback of the tracking results of moving objects is proposed. According to the achieved object tracking results, a frame image is divided into four kinds of regions, then a dual-layer background updating is done for these different regions with appropriate operations, which can significantly improve the quality of the background model. Based on the spatial relationship among the tracked objects, the predicted object blocks are merged into regions, among which adaptive segmentation thresholds are used for foreground segmentation. This adaptive threshold approach can efficiently avoid the erroneous holes and splitting in foreground segmentation. Our proposed approach is validated with several public data sets, which confirm its advantages over many existing approaches.