Abstract
Visual recognition of human actions in image sequences is an active field of research. However, most recent published methods use complex models and heuristics of the human body as well as to classify their actions. Our approach follows a different strategy. It is based on simple feature extraction from descriptors obtained from a visual tracking system. The tracking system is able to bring some useful information like position and size of the subject at every time step of a sequence, and in this paper we show that, the evolution of some of these features is enough to classify an action in most of the cases.
Keywords
- Support Vector Machine
- Memetic Algorithm
- Human Action Recognition
- Human Silhouette
- Continuous Hide Markov Model
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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Hernández, J., Montemayor, A.S., José Pantrigo, J., Sánchez, Á. (2011). Human Action Recognition Based on Tracking Features. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) Foundations on Natural and Artificial Computation. IWINAC 2011. Lecture Notes in Computer Science, vol 6686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21344-1_49
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DOI: https://doi.org/10.1007/978-3-642-21344-1_49
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