A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures
Journal of the ACM (JACM)
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Invariant features for 3-D gesture recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Combining Sensory and Symbolic Data for Manipulative Gesture Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
3D trajectory matching by pose normalization
Proceedings of the 13th annual ACM international workshop on Geographic information systems
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
Tracking of Human Body Parts using the Multiocular Contracting Curve Density Algorithm
3DIM '07 Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Hierarchical Matching of 3D Pedestrian Trajectories for Surveillance Applications
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
HBU'10 Proceedings of the First international conference on Human behavior understanding
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In this contribution we introduce a novel method for 3D trajectory based recognition and discrimination between different working actions and long-term motion prediction. The 3D pose of the human hand-forearm limb is tracked over time with a multi-hypothesis Kalman Filter framework using the Multiocular Contracting Curve Density algorithm (MOCCD) as a 3D pose estimation method. A novel trajectory classification approach is introduced which relies on the Levenshtein Distance on Trajectories (LDT) as a measure for the similarity between trajectories. Experimental investigations are performed on 10 real-world test sequences acquired from different viewpoints in a working environment. The system performs the simultaneous recognition of a working action and a cognitive long-term motion prediction. Trajectory recognition rates around 90% are achieved, requiring only a small number of training sequences. The proposed prediction approach yields significantly more reliable results than a Kalman Filter based reference approach.