Floating search methods in feature selection
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Multi-View Face Detection
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Face Cataloger: Multi-Scale Imaging for Relating Identity to Location
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
A joint system for single-person 2D-face and 3D-head tracking in CHIL seminars
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Automatic initialization for 3D soccer player tracking
Pattern Recognition Letters
Recovery and Reasoning About Occlusions in 3D Using Few Cameras with Applications to 3D Tracking
International Journal of Computer Vision
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Visual detection and tracking of humans in complex scenes is a challenging problem with a wide range of applications, for example surveillance and human-computer interaction. In many such applications, time-synchronous views from multiple calibrated cameras are available, and both frame-view and space-level human location information is desired. In such scenarios, efficiently combining the strengths of face detection and person tracking is a viable approach that can provide both levels of information required and improve robustness. In this paper, we propose a novel vision system that detects and tracks human faces automatically, using input from multiple calibrated cameras. The method uses an Adaboost algorithm variant combined with mean shift tracking applied on single camera views for face detection and tracking, and fuses the results on multiple camera views to check for consistency and obtain the three-dimensional head estimate. We apply the proposed system to a lecture scenario in a smart room, on a corpus collected as part of the CHIL European Union integrated project. We report results on both frame-level face detection and three-dimensional head tracking. For the latter, the proposed algorithm achieves similar results with the IBM “PeopleVision” system.