A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Retrieval
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The Visual Hull Concept for Silhouette-Based Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
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
Computer Vision and Image Understanding - Model-based and image-based 3D scene representation for interactive visalization
Fusion of Multi-View Silhouette Cues Using a Space Occupancy Grid
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Don't push me! Collision-avoiding swarms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Volumetric Descriptions of Objects from Multiple Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
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Background modeling algorithms are commonly used in camera setups for foreground object detection. Typically, these algorithms need adjustment of their parameters towards achieving optimal performance in different scenarios and/or lighting conditions. This is a tedious process requiring considerable effort by expert users. In this work we propose a novel, fully automatic method for the tuning of foreground detection parameters in calibrated multicamera systems. The proposed method requires neither user intervention nor ground truth data. Given a set of such parameters, we define a fitness function based on the consensus built from the multicamera setup regarding whether points belong to the scene foreground or background. The maximization of this fitness function through Particle Swarm Optimization leads to the adjustment of the foreground detection parameters. Extensive experimental results confirm the effectiveness of the adopted approach.