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Authors: Tobias Bolten 1 ; Regina Pohle-Fröhlich 1 and Klaus Tönnies 2

Affiliations: 1 Institute for Pattern Recognition, Hochschule Niederrhein, Krefeld, Germany ; 2 Department of Simulation and Graphics, University of Magdeburg, Germany

Keyword(s): Dynamic Vision Sensor, Instance Segmentation, Outdoor Environment.

Abstract: Event cameras are a new type of image sensor. The pixels of these sensors operate independently and asynchronously from each other. The sensor output is a variable rate data stream that spatio-temporally encodes the detection of brightness changes. This type of output and sensor operating paradigm poses processing challenges for computer vision applications, as frame-based methods are not natively applicable. We provide the first systematic evaluation of different state-of-the-art deep learning based instance segmentation approaches in the context of event-based outdoor surveillance. For processing, we consider transforming the event output stream into representations of different dimensionalities, including point-, voxel-, and frame-based variants. We introduce a new dataset variant that provides annotations at the level of instances per output event, as well as a density-based preprocessing to generate regions of interest (RoI). The achieved instance segmentation results show that the adaptation of existing algorithms for the event-based domain is a promising approach. (More)

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Paper citation in several formats:
Bolten, T.; Pohle-Fröhlich, R. and Tönnies, K. (2024). Instance Segmentation of Event Camera Streams in Outdoor Monitoring Scenarios. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 452-463. DOI: 10.5220/0012369100003660

@conference{visapp24,
author={Tobias Bolten. and Regina Pohle{-}Fröhlich. and Klaus Tönnies.},
title={Instance Segmentation of Event Camera Streams in Outdoor Monitoring Scenarios},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={452-463},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012369100003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Instance Segmentation of Event Camera Streams in Outdoor Monitoring Scenarios
SN - 978-989-758-679-8
IS - 2184-4321
AU - Bolten, T.
AU - Pohle-Fröhlich, R.
AU - Tönnies, K.
PY - 2024
SP - 452
EP - 463
DO - 10.5220/0012369100003660
PB - SciTePress