This paper presents comparative results of applying different architectures of generative classifiers (HMM, FHMM, CHMM, Multi-Stream HMM, Parallel HMM ) and discriminative classifier as Conditional Random Fields (CRFs) in human action sequence recognition. The models are fed with histogram of very informative features such as contours evolution and optical-flow. Motion orientation discrimination has been obtained tiling the bounding box of the subject and extracting features from each tile. We run our experiments on two well-know databases, KTH́s database and Weizmanńs. The results show that both type of models reach similar score, being the generative model better when used with optical flow features and being the discriminative one better when uses with shape-context features. © 2008 Springer-Verlag.
CITATION STYLE
Mendoza, M. Á., & Pérez De La Blanca, N. (2008). Applying space state models in human action recognition: A comparative study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5098 LNCS, pp. 53–62). https://doi.org/10.1007/978-3-540-70517-8_6
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