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
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Incorporating Object Tracking Feedback into Background Maintenance Framework
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
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
Robust Video-Based Surveillance by Integrating Target Detection with Tracking
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Robust background subtraction with foreground validation for urban traffic video
EURASIP Journal on Applied Signal Processing
Stationary target detection using the objectvideo surveillance system
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Kalman tracking with target feedback on adaptive background learning
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
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In video surveillance and long term scene monitoring applications, it is a challenging problem to handle slow-moving or stopped objects for motion analysis and tracking. We present a new framework by using two feedback mechanisms which allow interactions between tracking and background subtraction (BGS) to improve tracking accuracy, particularly in the cases of slow-moving and stopped objects. A publish-subscribe modular system that provides the framework for communication between components is described. The robustness and efficiency of the proposed method is tested on our real time video surveillance system. Quantitative performance evaluation is performed on a variety of sequences, including standard datasets. With the two feedback mechanisms enabled together, significant improvement in tracking performance are demonstrated particularly in handling slow moving and stopped objects.