Summary
In this paper, we address the problem of human activity classification from videos, giving a special emphasis to feature extraction and good feature selection. Due to the cut down in cameras cost that have been in the last years, these kind of systems are becoming popular for their wide application area. Taking a video blob tracker output, a feature extraction process is defined to extract an extensive feature set, that is filtered in a later step to select the best features present. Three different type of classifiers are trained with the result feature set and results are shown.
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Cilla, R., Patricio, M.A., Berlanga, A., Molina, J.M. (2009). On the Process of Designing an Activity Recognition System Using Symbolic and Subsymbolic Techniques. In: Corchado, J.M., Rodríguez, S., Llinas, J., Molina, J.M. (eds) International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008). Advances in Soft Computing, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85863-8_86
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DOI: https://doi.org/10.1007/978-3-540-85863-8_86
Publisher Name: Springer, Berlin, Heidelberg
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