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  4. Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework
 
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2023
Conference Paper
Title

Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework

Abstract
The ability to detect learned objects regardless of their appearance is crucial for autonomous systems in real-world applications. Especially for detecting humans, which is often a fundamental task in safety-critical applications, it is vital to prevent errors. To address this challenge, we propose a self-monitoring framework that allows for the perception system to perform plausibility checks at runtime. We show that by incorporating an additional component for detecting human body parts, we are able to significantly reduce the number of missed human detections by factors of up to 9 when compared to a baseline setup, which was trained only on holistic person objects. Additionally, we found that training a model jointly on humans and their body parts leads to a substantial reduction in false positive detections by up to 50 percent compared to training on humans alone. We performed comprehensive experiments on the publicly available datasets DensePose and Pascal VOC in order to demonstrate the effectiveness of our framework.
Author(s)
Schwaiger, Franziska  
Fraunhofer-Institut für Kognitive Systeme IKS  
Matic-Flierl, Andrea
Fraunhofer-Institut für Kognitive Systeme IKS  
Roscher, Karsten  
Fraunhofer-Institut für Kognitive Systeme IKS  
Günnemann, Stephan
Technische Universität München  
Mainwork
34th IEEE Intelligent Vehicles Symposium, IV 2023. Proceedings  
Project(s)
IKS-Aufbauprojekt  
Safe.trAIn
Funder
Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie
Bundesministerium für Wirtschaft und Klimaschutz  
Conference
Intelligent Vehicles Symposium 2023  
Open Access
DOI
10.1109/IV55152.2023.10186644
10.24406/publica-1718
File(s)
Schwaiger_PreventingErrorsInPersonDetectionAPartBasedSelfMonitoringFramework_2306_IV_AcceptedVersion.pdf (3.19 MB)
Rights
Under Copyright
Language
English
Fraunhofer-Institut für Kognitive Systeme IKS  
Fraunhofer Group
Fraunhofer-Verbund IUK-Technologie  
Keyword(s)
  • object detection

  • runtime monitoring

  • person detection

  • intelligent vehicle

  • IV

  • autonomous systems

  • safety-critical application

  • self-monitoring framework

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