Pfinder: Real-Time Tracking of the Human Body
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 Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
View-Based Detection and Analysis of Periodic Motion
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
A Background Layer Model for Object Tracking Through Occlusion
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
Detecting social interactions of the elderly in a nursing home environment
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Dynamic Graph Cuts for Efficient Inference in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video-Based Fall Detection in the Home Using Principal Component Analysis
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Detecting abnormal human behaviour using multiple cameras
Signal Processing
A matching-based approach for human motion analysis
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring
IEEE Transactions on Circuits and Systems for Video Technology
Broadcast Court-Net Sports Video Analysis Using Fast 3-D Camera Modeling
IEEE Transactions on Circuits and Systems for Video Technology
Intelligent trainee behavior assessment system for medical training employing video analysis
Pattern Recognition Letters
Fast saliency-aware multi-modality image fusion
Neurocomputing
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This paper aims at generating an automated way to evaluate the team-behavior of trainees in a delivery simulation course using video-processing techniques with emphasis on multiple people tracking. The paper is composed of two interacting, but clearly separated stages: moving people segmentation and multiple people tracking. At people segmentation stage, the combination of the Gaussian Mixture Model (GMM) and the Dynamic Markov Random Fields (DMRF) technique helps to extract the foreground pixels. For a better extraction of the human silhouettes, the energy function of DMRF is extended with texture information. At multiple people tracking stage, we concentrate on solving human-occlusion problem caused by interacting persons based on silhouette data and a non-linear regression model. Our model effectively transfers the person location problem during the occlusion into the finding of the local maximum points on a smooth curve, so that visual persons in the partial or complete occlusion can still be precisely captured. We have compared our algorithm with two other popular tracking algorithms: mean-shift and particle-filter. Experimental results reveal that the correctness of our method is much higher than the mean-shift algorithm and slightly lower than a particle-filter, however, with the major benefit of being a factor of 10---15 faster in computing.