Online Monitoring for Neural Network Based Monocular Pedestrian Pose Estimation
Name
2005.05451.pdf
Description
Accepted version
Size
592.17 KB
Format
Adobe PDF
Checksum (MD5)
5ff859ec0e108dc0be5cc86d523f70d7
Author(s)
Gupta, Arjun
Carlone, Luca
Date Issued
December 2020
Journal
2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
Publisher
IEEE
Citation
Gupta, Arjun and Carlone, Luca. 2020. "Online Monitoring for Neural Network Based Monocular Pedestrian Pose Estimation." 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020.
Version
Author's final manuscript
Abstract
© 2020 IEEE. Several autonomy pipelines now have core components that rely on deep learning approaches. While these approaches work well in nominal conditions, they tend to have unexpected and severe failure modes that create concerns when used in safety-critical applications, including self-driving cars. There are several works that aim to characterize the robustness of networks offline, but currently there is a lack of tools to monitor the correctness of network outputs online during operation. We investigate the problem of online output monitoring for neural networks that estimate 3D human shapes and poses from images. Our first contribution is to present and evaluate model-based and learning-based monitors for a human-pose-and-shape reconstruction network, and assess their ability to predict the output loss for a given test input. As a second contribution, we introduce an Adversarially-Trained Online Monitor (ATOM) that learns how to effectively predict losses from data. ATOM dominates model-based baselines and can detect bad outputs, leading to substantial improvements in human pose output quality. Our final contribution is an extensive experimental evaluation that shows that discarding outputs flagged as incorrect by ATOM improves the average error by 12.5%, and the worst-case error by 126.5%.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ITSC45102.2020.9294609
