Work

Depth and thermal information fusion for head tracking using particle filter in a fall detection context

Year: 2020

Type: article

Abstract: The security of elderly people living alone is a major issue. A system that detects anomalies can be useful for both individual and retirement homes. In this paper, we present an adaptive human tracking method built on particle filter, using depth and thermal information based on the velocity and t he position of the head. The main contribution of this paper is the fusion of information to improve tracking. For each frame, there is a new combination of coefficients for each particle based on an adaptive weighting. Results show that the tracking method can deal with the cases of fast motion (fall), partial occultation and scale variation. To assess the impact of fusion on the tracking process, the robustness and accuracy of the method are tested on a variety of challenging scenarios with or without depth-thermal fusion.

Source: Integrated Computer-Aided Engineering

DOI: 10.3233/ica-190615

Authors: Imen Halima, Jean-Marc Laferté, Geoffroy Cormier, Alain-Jérôme Fougères, Jean-Louis Dillenseger

Institutions: Inserm, Laboratoire Traitement du Signal et de l'Image, ECAM Rennes Louis de Broglie, Université de Rennes

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Agence Nationale de la Recherche

Award: FALL DETECTION AND PREVENTION USING LOW COST DEPTH AND THERMAL SENSORS