Towards a unified framework for tracking and analysis of human motion
Abstract
We propose a framework for detecting, tracking and analyzing non-rigid motion based on learned motion patterns. The framework features an appearance based approach to represent the spatial information and hidden Markov models (HMM) to encode the temporal dynamics of the time varying visual patterns. The low level spatial feature extraction is fused with the temporal analysis, providing a unified spatio-temporal approach to common detection, tracking and classification problems. This is a promising approach for many classes of human motion patterns. Visual tracking is achieved by extracting the most probable sequence of target locations from a video stream using a combination of random sampling and the forward procedure from HMM theory. The method allows us to perform a set of important tasks such as activity recognition, gait-analysis and keyframe extraction. The efficacy of the method is shown on both natural and synthetic test sequences.
Publication Title
Proceedings - IEEE Workshop on Detection and Recognition of Events in Video, EVENT 2001
Recommended Citation
Krahnstöver, N., Yeasin, M., & Sharma, R. (2001). Towards a unified framework for tracking and analysis of human motion. Proceedings - IEEE Workshop on Detection and Recognition of Events in Video, EVENT 2001, 47-54. https://doi.org/10.1109/EVENT.2001.938865
