防止行人检测中的错误：一种基于部件的自监控框架
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework
AI总结：
本文提出一种基于人体部件检测的自监控框架，通过在运行时进行合理性检查，显著减少行人检测中的漏检和误检，并在DensePose和Pascal VOC上验证了有效性。
摘要：
无论外观如何都能检测已学习目标的能力，对于现实应用中的自主系统至关重要。尤其是行人检测，通常是安全关键应用中的基础任务，防止错误至关重要。为应对这一挑战，我们提出了一种自监控框架，使感知系统能够在运行时执行合理性检查。我们表明，通过引入一个额外的人体部件检测组件，与仅针对整体行人目标训练的基线设置相比，我们能够将漏检行人数量显著减少，降幅最高可达9倍。此外，我们发现，联合训练行人和人体部件的模型，与仅训练行人的模型相比，假阳性检测数量可大幅减少最多50%。我们在公开数据集DensePose和Pascal VOC上进行了全面实验，以证明该框架的有效性。代码可在 https://github.com/FraunhoferIKS/smf-object-detection 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
