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

TL;DR

This paper introduces a self-monitoring framework with body-part detection to enhance person detection accuracy in autonomous systems, significantly reducing missed detections and false positives.

Contribution

It presents a novel part-based self-monitoring framework that improves person detection robustness by runtime plausibility checks and joint training on humans and body parts.

Findings

Missed detections reduced by up to 9 times.

False positives decreased by up to 50%.

Effective on DensePose and Pascal VOC datasets.

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% 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.