Abstract
The human activity recognition in video sequences is a field where many types of classifiers have been used as well as a wide range of input features that feed these classifiers. This work has a double goal. First of all, we extracted the most relevant features for the activity recognition by only utilizing motion features provided by a simple tracker based on the 2D centroid coordinates and the height and width of each person’s blob. Second, we present a performance comparison among seven different classifiers (two Hidden Markov Models (HMM), a J.48 tree, two Bayesian classifiers, a classifier based on rules and a Neuro-Fuzzy system). The video sequences under study present four human activities (inactive, active, walking and running) that have been manual labeled previously. The results show that the classifiers reveal different performance according to the number of features employed and the set of classes to sort. Moreover, the basic motion features are not enough to have a complete description of the problem and obtain a good classification.
Funded by CICYT TEC2005-07186, CAM 15 MADRINET S- 0505/TIC/0255, FOMENTO SINPROB.
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Pérez, Ó., Piccardi, M., García, J., Molina, J.M. (2007). Comparison of Classifiers for Human Activity Recognition. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_21
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DOI: https://doi.org/10.1007/978-3-540-73055-2_21
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