Whole body motion primitive segmentation from monocular video

Authors:
Dana Kulic;Dongheui Lee;Yoshihiko Nakamura
Affiliations:
Department of Mechano-Informatics, University of Tokyo, Tokyo, Japan;Department of Mechano-Informatics, University of Tokyo, Tokyo, Japan;Department of Mechano-Informatics, University of Tokyo, Tokyo, Japan
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

Citing 5
Cited 1

Automated Derivation of Primitives for Movement Classification

Autonomous Robots
Markerless tracking of complex human motions from multiple views

Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Extensions of vector quantization for incremental clustering

Pattern Recognition
Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains

International Journal of Robotics Research
Hierarchical Clustering of Time-Series Data Streams

IEEE Transactions on Knowledge and Data Engineering

Comparative study of representations for segmentation of whole body human motion data

IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a novel approach for motion primitive segmentation from continuous full body human motion captured on monocular video. The proposed approach does not require a kinematic model of the person, nor any markers on the body. Instead, optical flow computed directly in the image plane is used to estimate the location of segment points. The approach is based on detecting tracking features in the image based on the Shi and Thomasi algorithm [1]. The optical flow at each feature point is then estimated using the Lucas Kanade Pyramidal Optical Flow estimation algorithm [2]. The feature points are clustered and tracked on-line to find regions of the image with coherent movement. The appearance and disappearance of these coherent clusters indicates the start and end points of motion primitive segments. The algorithm performance is validated on full body motion video sequences, and compared to a joint-angle, motion capture based approach. The results show that the segmentation performance is comparable to the motion capture based approach, while using much simpler hardware and at a lower computational effort.