Geintra

During the last decades topics such as video analysis
and image understanding techniques have experimented
an important evolution due to its inclusion in applications
such as surveillance, intelligent spaces and assisted living. 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
examples, but in most cases these datasets have not been created
neither specifically for the mentioned applications, nor in realistic
scenarios. Within this context, in this paper we present a work
that implements a solution for falling detection from monocular
video sequences acquired with an standard video-camera. It
includes, both the multi-person detector and tracker in realistic
scenarios, and the action classifier for each of the detected
persons. Besides, it is also presented a newly created dataset with
realistic scenes specifically designed for surveillance applications.
Scientific soundness and development of the proposed algorithm
and its results and 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.