The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this paper the specific problem of falls of old adults is addressed by devising a technological solution for monitoring these users. Video cameras, accelerometers and GPS sensors are combined in a multi-modal approach to monitor humans inside and outside the domestic environment. Machine learning techniques are used to detect falls and classify activities from accelerometer data. Video feeds and GPS are used to provide location inside and outside the domestic environment. It results in a monitoring solution that does not imply the confinement of the users to a closed environment.

This paper investigates the robustness of a new thermal-infrared pedestrian detection system under different outdoor environmental conditions. In first place the algorithm for pedestrian ROI extraction in thermal-infrared video based on both thermal and motion information is introduced. Then, the evaluation of the proposal is detailed after describing the complete thermal and motion information fusion. In this sense, the environment chosen for evaluation is described, and the twelve test sequences are specified. For each of the sequences captured from a forward-looking infrared FLIR A-320 camera, the paper explains the weather and light conditions under which it was captured. The results allow us to draw firm conclusions about the conditions under which it can be affirmed that it is efficient to use our thermal-infrared proposal to robustly extract human ROIs.

This paper introduces a confidence measure scheme in a bimodal camera setup for automatically selecting visible-light or a thermal infrared in response to natural environmental changes. The purpose of the setup is to robustly detect people in dynamic outdoor scenarios under very different conditions. For this purpose, two efficient segmentation algorithms, one dedicated to the visible-light spectrum and another one to the thermal infrared spectrum, are implemented. The segmentation algorithms are applied to five different video sequences recorded under very different environmental conditions. The results of the segmentation in both spectra allow one to establish the best-suited confidence interval thresholds and to validate the overall approach. Indeed, the confidence measures take linguistic values LOW , M EDIU M and HIGH, depending on the reliability of the results obtained in visible-light, as well as in thermal infrared video.

a b s t r a c tIn this paper, a new approach to real-time people segmentation through processing images captured by an infrared camera is introduced. The approach starts detecting human candidate blobs processed through traditional image thresholding techniques. Afterwards, the blobs are refined with the objective of validating the content of each blob. The question to be solved is if each blob contains one single human candidate or more than one. If the blob contains more than one possible human, the blob is divided to fit each new candidate in height and width.

Fall detection, especially for elderly people, is a challenging problem which demands new products and technologies. In this paper a fuzzy model for fall detection and inactivity monitoring in infrared video is presented. The classification features proposed include geometric and kinematic parameters associated with more or less sudden changes in the tracked human-related regions of interest. A complete segmentation and tracking algorithm for infrared video as well as a fuzzy fall detection and confirmation algorithm are introduced. The proposed system is capable of identifying true and false falls, enhanced with inactivity monitoring aimed at confirming the need for medical assistance and/or care. The fall indicators used as well as their fuzzy model is explained in detail. The fuzzy model has been tested for a wide number of static and dynamic falls, demonstrating exciting initial results.