On supervised human activity analysis for structured environments

Authors: Arbab-Zavar, B., Bouchrika, I., Carter, J.N. and Nixon, M.S.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 6455 LNCS

Issue: PART 3

Pages: 625-634

eISSN: 1611-3349

ISBN: 9783642172762

ISSN: 0302-9743

DOI: 10.1007/978-3-642-17277-9_64


We consider the problem of developing an automated visual solution for detecting human activities within industrial environments. This has been performed using an overhead view. This view was chosen over more conventional oblique views as it does not suffer from occlusion, but still retains powerful cues about the activity of individuals. A simple blob tracker has been used to track the most significant moving parts i.e. human beings. The output of the tracking stage was manually labelled into 4 distinct categories: walking; carrying; handling and standing still which are taken together from the basic building blocks of a higher work flow description. These were used to train a decision tree using one subset of the data. A separate training set is used to learn the patterns in the activity sequences by Hidden Markov Models (HMM). On independent testing, the HMM models are applied to analyse and modify the sequence of activities predicted by the decision tree. © 2010 Springer-Verlag.

Source: Scopus