Tracking and data association
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
A Sampling Algorithm for Tracking Multiple Objects
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Tracking and Segmenting People in Varying Lighting Conditions Using Colour
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
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
Kalman filters for time delay of arrival-based source localization
EURASIP Journal on Applied Signal Processing
Multi- and single view multiperson tracking for smart room environments
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Tracking multiple speakers with probabilistic data association filters
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Probabilistic integration of sparse audio-visual cues for identity tracking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Towards high-level human activity recognition through computer vision and temporal logic
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Person tracking based on a hybrid neural probabilistic model
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
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In this paper, two multimodal systems for the tracking of multiple users in smart environments are presented. The first is a multi-view particle filter tracker using foreground, color and special upper body detection and person region features. The other is a wide angle overhead view person tracker relying on foreground segmentation and model-based blob tracking. Both systems are completed by a joint probabilistic data association filter-based source localizer using the input from several microphone arrays. While the first system fuses audio and visual cues at the feature level, the second one incorporates them at the decision level using state-based heuristics.The systems are designed to estimate the 3D scene locations of room occupants and are evaluated based on their precision in estimating person locations, their accuracy in recognizing person configurations and their ability to consistently keep track identities over time.The trackers are extensively tested and compared, for each separate modality and for the combined modalities, on the CLEAR 2007 Evaluation Database.