Human activity monitoring for falling detection. A realistic framework

摘要

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.