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Time-of-Flight Depth Sensing for Automotive Safety and Smart Building Applications: The VIZTA Project

Jason Raphael Rambach; Bruno Mirbach; Yuriy Anisimov; Didier Stricker
In: IEEE (Hrsg.). IEEE Access (IEEE), Vol. 11, Pages 105819-105829, IEEE, 10/2023.


Time-of-Flight (ToF) can be an advantageous sensing modality for several indoor applications, used alone or in combination with other sensors such as RGB cameras. As part of the research project VIZTA (Vision, Identification, with Z-sensing Technologies and key Applications), we developed methods using Machine Learning algorithms with ToF depth measurements as inputs to address two key areas of applications, in- car cabin monitoring (person detection and segmentation) and smart building monitoring (person counting and anomaly detection). In this article, we discuss the entire research approach followed in VIZTA, from setting up the experimental environments for collecting data and creating the VIZTA public datasets, to developing Deep Learning algorithms tailored to ToF data, used either in 2D depth map or 3D point cloud format. We discuss the advantages and challenges of using ToF-data, as well as the lessons learned during the evaluation and benchmarking of our methods.