Evaluation of Human Pose Recognition and Object Detection Technologies and Architecture for Situation-Aware Robotics Applications in Edge Computing Environment

Realization of situation-awareness for autonomous robotics applications in edge computing environment is challenging. First, computing capabilities of edge devices are limited, which must be considered in the execution of machine learning (ML)-based solutions. Second, many technologies are available for realizing situation-aware capabilities, but comparison and integration of solutions creates additional challenges. Third, existing ML-based models are often not directly applicable for realizing custom applications, and model(s) may need to be re-trained with new data. The contribution of this paper is efficiency and feasibility evaluation of human pose recognition and object detection technologies in edge computing environment. Several lessons learnt covering constraints are presented regarding feasibility of the experimented technologies and data sets. The efficiency evaluation results indicated that simultaneous human pose recognition (Google’s Movenet) and object detection (Yolov5) on Jetson AGX Xavier achieved ~13-16 FPS, while GPU and CPU utilization remained at a medium level, and most of the memory remained unused (< 44 %). Object concept and human pose concept activation algorithms may be considered as an additional contribution. Realized architecture design of the prototyped system in multiple computing environments can be considered as a partial evaluation of a ML-based big data reference architecture.