Abstract
In this paper we address the problem of human action recognition from a single training sequence per class using a modified version of the Hidden Markov Model. Inspired by codebook approaches in object and scene categorization, we first construct a codebook of possible discrete observations by applying a clustering algorithm to all samples from all classes. The number of clusters defines the size of the codebook. Given a new observation, we assign to it a probability to belong to every cluster, i.e., to correspond to a discrete value of the codebook. In this sense, we change the ‘winner takes all’ rule in the discrete-observation HMM for a distributed probability of membership. It implies the modification of the Baum-Welch algorithm for training discrete HMM to be able to deal with fuzzy observations. We compare our approach with other models such as, dynamic time warping (DTW), continuous-observation HMM, Conditional Random Fields (CRF) and Hidden Conditional Random Fields (HCRF) for human action recognition.
Keywords
- Hide Markov Model
- Training Sequence
- Dynamic Time Warping
- Conditional Random Field
- Human Action Recognition
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Orrite, C., Rodríguez, M., Montañés, M. (2011). One-Sequence Learning of Human Actions. In: Salah, A.A., Lepri, B. (eds) Human Behavior Understanding. HBU 2011. Lecture Notes in Computer Science, vol 7065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25446-8_5
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DOI: https://doi.org/10.1007/978-3-642-25446-8_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25445-1
Online ISBN: 978-3-642-25446-8
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