Machine Learning
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
Estimating Human Body Configurations Using Shape Context Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Joint Probabilistic Techniques for Tracking Multi-Part Objects
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Ghost3D: Detecting Body Posture and Parts Using Stereo
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Inference of Human Postures by Classification of 3D Human Body Shape
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Journal of Cognitive Neuroscience
Multi-resolution real-time stereo on commodity graphics hardware
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Mutual information based registration of multimodal stereo videos for person tracking
Computer Vision and Image Understanding
Learning, detection and representation of multi-agent events in videos
Artificial Intelligence
Adaptive multi-modal stereo people tracking without background modelling
Journal of Visual Communication and Image Representation
Toward a sentient environment: real-time wide area multiple human tracking with identities
Machine Vision and Applications
People detection and tracking with multiple stereo cameras using particle filters
Journal of Visual Communication and Image Representation
Robust pedestrian detection and tracking in crowded scenes
Image and Vision Computing
Vision-Based Markerless Gaming Interface
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Tracking a moving hypothesis for visual data with explicit switch detection
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Learning to recognize complex actions using conditional random fields
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Improving usability by adding security to video conferencing systems
FC'07/USEC'07 Proceedings of the 11th International Conference on Financial cryptography and 1st International conference on Usable Security
Bayesian loop for synergistic change detection and tracking
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Integrated tracking and recognition of human activities in shape space
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Shape from pairwise silhouettes for plan-view map generation
Image and Vision Computing
Robust object tracking in crowd dynamic scenes using explicit stereo depth
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
On-line Support Vector Regression of the transition model for the Kalman filter
Image and Vision Computing
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Plan-view projection of real-time depth imagery can improve the statistics of its intrinsic 3D data, and allows for cleaner separation of occluding and closely-interacting people. We build a probabilistic, real-time multi-person tracking system upon a plan-view image substrate that well preserves both shape and size information of foreground objects. The tracking's robustness derives in part from its "plan-view template" person models, which capture detailed properties of people's body configurations. We demonstrate that these same person models - obtained with a single compact stereo camera unit - may also be used for fast recognition of body pose and activity. Principal components analysis is used to extract plan-view "eigenposes", onto which person models, extracted during tracking, are projected to produce a compact representation of human body configuration. We then formulate pose recognition as a classification problem, and use support vector machines (SVMs) to quickly distinguish between, for example, different directions people are facing, and different body poses such as standing, sitting, bending over, crouching, and reaching. The SVM outputs are transformed to probabilities and integrated across time in a probabilistic framework for real-time activity recognition