Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework
This work addresses a safety-critical issue for autonomous systems by improving human detection reliability, though it is incremental as it builds on existing object detection methods.
The paper tackles the problem of preventing errors in person detection for autonomous systems by proposing a part-based self-monitoring framework, resulting in up to a 9-fold reduction in missed detections and up to 50% fewer false positives compared to baseline methods.
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.