TY - GEN
T1 - Preventing Errors in Person Detection
T2 - 34th IEEE Intelligent Vehicles Symposium, IV 2023
AU - Schwaiger, Franziska
AU - Matic, Andrea
AU - Roscher, Karsten
AU - Gunnemann, Stephan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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% 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. Code is available at https://github.com/FraunhoferIKS/smf-object-detection.
AB - 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% 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. Code is available at https://github.com/FraunhoferIKS/smf-object-detection.
UR - http://www.scopus.com/inward/record.url?scp=85168000567&partnerID=8YFLogxK
U2 - 10.1109/IV55152.2023.10186644
DO - 10.1109/IV55152.2023.10186644
M3 - Conference contribution
AN - SCOPUS:85168000567
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
BT - IV 2023 - IEEE Intelligent Vehicles Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 June 2023 through 7 June 2023
ER -