Fundamentals of digital image processing
Fundamentals of digital image processing
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Automatic image annotation using adaptive color classification
Graphical Models and Image Processing
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Three-Dimensional Human Body Model Acquisition from Multiple Views
International Journal of Computer Vision
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Estimating anthropometry and pose from a single uncalibrated image
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Object Tracking Using Adaptive Color Mixture Models
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Skin-Color Modeling and Adaptation
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
3-D model-based tracking of humans in action: a multi-view approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Pfinder: real-time tracking of the human body
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Cardboard People: A Parameterized Model of Articulated Image Motion
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Recognition of Human Interaction Using Multiple Features in Grayscale Images
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Tracking and Recognizing Two-Person Interactions in Outdoor Image Sequences
WOMOT '01 Proceedings of the IEEE Workshop on Multi-Object Tracking (WOMOT'01)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A convex penalty method for optical human motion tracking
IWVS '03 First ACM SIGMM international workshop on Video surveillance
Temporal spatio-velocity transform and its application to tracking and interaction
Computer Vision and Image Understanding - Special issue on event detection in video
Efficient image segmentation for region-based motion estimation and compensation
IEEE Transactions on Circuits and Systems for Video Technology
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Understanding human interactions with track and body synergies (TBS) captured from multiple views
Computer Vision and Image Understanding
Semantic Representation and Recognition of Continued and Recursive Human Activities
International Journal of Computer Vision
Robust human-computer interaction system guiding a user by providing feedback
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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This paper presents a framework to simultaneously segment and track multiple body parts of interacting humans in the presence of mutual occlusion and shadow. The framework uses multiple free-form blobs and a coarse model of the human body. The color image sequence is processed at three levels: pixel level, blob level, and object level. A Gaussian mixture model is used at the pixel level to train and classify individual pixel based on color. Relaxation labeling in an attribute relational graph (ARG) is used at the blob level to merge the pixels into coherent blobs and to represent inter-blob relations. A twofold tracking scheme is used that consists of blob-to-blob matching in consecutive frames and blob-to-body-part association within a frame. The tracking scheme resembles multi-target, multiassociation tracking (MMT). A coarse model of the human body is applied at the object level as empirical domain knowledge to resolve ambiguity due to occlusion and to recover from intermittent tracking failures. The result is 'ARG-MMT': 'attribute relational graph based multi-target, multi-association tracker.' The tracking results are demonstrated for various sequences including 'punching,' 'hand-shaking,' 'pushing,' and 'hugging' interactions between two people. This ARG-MMT system may be used as a segmentation and tracking unit for a recognition system for human interactions.