Active Learning Assisted Self-reconfigurable Activity Recognition in Dynamic Environment
Date Issued
2008
Date
2008
Author(s)
Ho, Yu-Chen
Abstract
This thesis addresses the problem of learning and recognizing human daily activities in smart environment. Most approaches offline learn the activity model and recognize the activity in an online phase. However, the activity models can be outdated when the human behavior and environment deployment change. It is a tedious and error-prone job to recollect data for retraining the activity models. In such case, it is important to adapt the learnt activity models under one context to another context without too much supervision. In this thesis, we present a self-reconfigurable approach for activity recognition can reconfigure a previously learned activity model to infer multiple activities under a dynamic environment meanwhile requiring minimal human supervision for labeling training data.
Subjects
Activity Recognition
Probabilistic Reasoning
Dynamic Bayesian Network
Active Learning
Type
thesis
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