VRU Pose-SSD: Multiperson Pose Estimation For Automated Driving
Keywords:Autonomous Vehicles, Computer Vision, Pose Estimation
AbstractWe present a fast and efficient approach for joint person detection and pose estimation optimized for automated driving (AD) in urban scenarios. We use a multitask weight sharing architecture to jointly train detection and pose estimation. This modular architecture allows us to accommodate different downstream tasks in the future. By systematic large-scale experiments on the Tsinghua-Daimler Urban Pose Dataset (TDUP), we obtain multiple models with varying accuracy-speed trade-offs. We then quantize and optimize our network for deployment and present a detailed analysis of the efficacy of the algorithm. We introduce a two-stage evaluation strategy, which is more suitable for AD and achieve a significant performance improvement in comparison to state-of-the-art approaches. Our optimized model runs at 52~fps on full HD images and still reaches a competitive performance of 32.25~LAMR. We are confident that our work serves as an enabler to tackle higher-level tasks like VRU intention estimation and gesture recognition, which rely on stable pose estimates and will play a crucial role in future AD systems.
How to Cite
Kumar, C., Ramesh, J., Chakraborty, B., Raman, R., Weinrich, C., Mundhada, A., Jain, A., & Flohr, F. B. (2021). VRU Pose-SSD: Multiperson Pose Estimation For Automated Driving. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15331-15338. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17800
IAAI Technical Track on Emerging Applications of AI