Reconstruction of articulated objects from point correspondences in a single uncalibrated image
Computer Vision and Image Understanding
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
Uncalibrated Motion Capture Exploiting Articulated Structure Constraints
International Journal of Computer Vision
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Singularity Analysis for Articulated Object Tracking
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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)
Model-Based Human Body Tracking
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
IEEE Intelligent Systems
Motion-Based Recognition of Pedestrians
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Ambient Intelligence: A Novel Paradigm
Ambient Intelligence: A Novel Paradigm
Recovering 3D Human Body Configurations Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extending surveillance systems capabilities using BDI cooperative sensor agents
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Multi-agent framework in visual sensor networks
EURASIP Journal on Applied Signal Processing
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This work is placed in the context of computer vision and ubiquitous multimedia access. It deals with the development of an automated system for human behavior analysis and prediction using context features as a representative descriptor of human posture. In our proposed method, an action is composed of a series of features over time. Therefore, time sequential images expressing human action are transformed into a feature vector sequence. Then the feature is transformed into symbol sequence. For that purpose, we design a posture codebook, which contains representative features of each action type and define distances to measure similarity between feature vectors. The system is also able to predict next performed motion. This prediction helps to evaluate and choose current action to show.