During the last decades topics such as video analysis and
image understanding have acquired a big importance due to its inclusion
in applications such as security, intelligent spaces, assistive living and focused marketing. In order to validate all related works different datasets
have been distributed within the research community: CAVIAR, KTH,
Weizmann, INRIA or MuHAVI are some of the most well-known ex-
amples, but in most cases these datasets have not been created for the
surveillance application in realistic scenes of interest. Within this context,
here we present a work that implements a solution for multiple persons'
action recognition in monocular video sequences, focused on surveillance
applications. Besides, it is also presented a newly created dataset with
realistic scenes specifically designed for commercial applications. Development and results of the proposed algorithm and its validation, both
within well-known datasets as CAVIAR and KTH and within the one
ad-hoc generated for the applications of interest, are discussed in the
paper.
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