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B. Kaluža. Reducing Spurious Activity Transitions in a Sequence of Movement. 18th International Electrotechnical and Computer Science Conference, ERK 2009 ,21st - 23th September,Portoroz-Slovenia.
Abstract:
Activity recognition is a fundamental task for analyzing human behaviour and it is usually achieved with a classifier. Although the classifier might be very accurate, it may still produce some false detections and consequently, spurious state transitions – the transitions that do not occur in reality. This paper examines two approaches for reducing spurious activity transitions. The first approach is based on cost-augment grammar classification namely Sequential Grammar-based Classifier, while the second approach uses hidden Markov models. The paper outlines a basic theoretical background of the methods and describes the implementation procedure. The results showed that both methods successfully reduced spurious transitions and improved classification accuracy.
