Prof. Jürgen Apfelbeck Profile

Prof. Jürgen Apfelbeck

at Hochschule Bonn-Rhein-Sieg

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 14 November 2021 Presentation + Paper

Evaluation of a concept for classification of micromobility vehicles based on thermal-infrared imaging and neuromorphic processing

Bastian Stahl, Robert Lange, Jürgen Apfelbeck

Proceedings Volume 11914, 119140J (2021) https://doi.org/10.1117/12.2601857

KEYWORDS: Sensors, Thermography, Image segmentation, Image processing, Neurons, Long wavelength infrared, Imaging systems, Infrared sensors

Read Abstract +

Current trends of environmental awareness combined with a focus on personal fitness and health, motivates many people to switch from cars and public transport to micromobility solutions, namely bicycles, electric bicycles, cargo bikes, or scooters. To accommodate urban planning for these changes, cities, and communities need to know how many (micromobility) vehicles are on the road. In this work, we design a concept for a compact, mobile, and energy-efficient system to classify and count micromobility vehicles. We aim for a battery-powered system, which can be installed in a short time by a single person and is non-invasive for the ground or traffic. For this purpose, we investigate a system architecture consisting of uncooled long-wave infrared (LWIR) imaging sensors and a neuromorphic co-processor. This processor can perform classifications in a fixed time of 16,2μs without requiring additional hardware for learning. It can adapt its knowledge base to new environments without external updates. Our system is aimed to achieve adequate accuracy only using a low-resolution sensor and moderate processing power. We will show that an object representation of only 256 Bytes is sufficient to achieve a four-class classification accuracy higher than 80%, with an overall power consumption of less than 7mW. We estimate a total operating time of at least two weeks using a common 12V 17Ah lead battery.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years