Pfinder: Real-Time Tracking of the Human Body
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
A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Multi lane vehicle orientation extractions using multi views from roadside cameras
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
| Hi-index | 0.00 |
Background subtraction is the first step for video surveillance. Existing methods almost all update their background models with a constant learning rate, which makes them not adaptive to some complex situations, e.g., crowded scenes or objects staying for a long time. In this paper, a novel framework which integrates both positive and negative feedbacks to control the learning rate is proposed. The negative feedback comes from background contextual analysis and the positive feedback comes from the foreground region analysis. Two descriptors of global contextual features are proposed and the visibility measures of background regions are derived based on contextual descriptors. Spatial-temporal features of the foreground regions are exploited. Fusing both positive and negative feedbacks, suitable strategy of background updating for specified surveillance task can be implemented. Three strategies for short-term, selective and long-term surveillance have been implemented and tested. Improved results compared with conventional background subtraction have been obtained.