Occlusion Estimation in 3D Point Clouds using Visual Data from Home Care Scenarios

David Laule, J. Diesel, Mattias Paul Heinrich


Today, home care monitoring systems are implemented more frequently, which is mainly due to the increasing number of elderly people and the reduced number of medical staff. This makes it all the more important that these systems are reliable and safe for its users. In this paper, we propose a risk management feature in form of an Occlusion State Index (OSI) based on the overall occlusion within an observed scene that can be integrated into an in-house developed smart home care monitor prototype. To implement and evaluate the proposed feature, visual data is acquired within a home care test scenario using two Kinect 2.0 depth cameras. After preprocessing, the recorded depth information is merged into a point cloud from which a scene occlusion map is computed based on the ray box intersection algorithm and the fast voxel traversal algorithm. Finally an OSI is computed depending on the amount of occluded voxels in a 3D point cloud.

Original languageEnglish
Title of host publicationStudent Conference 2018: 7th Conference on Medical Engineering Science and 3rd Conference on Medical Informatics and 1st Conference on Biomedical Engineering
EditorsThorsten Buzug, Heinz Handels, Stephan Klein
Number of pages4
PublisherInfinite Science Publishing
Publication date2018
ISBN (Print)978-3-945954-47-8
Publication statusPublished - 2018
EventStudent Conference 2018 - Universität zu Lübeck, Lübeck, Germany
Duration: 07.02.201709.03.2018


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