Christakos, P.; Petrellis, N.; Mousouliotis, P.; Keramidas, G.; Antonopoulos, C.P.; Voros, N. A High Performance and Robust FPGA Implementation of a Driver State Monitoring Application. Sensors2023, 23, 6344.
Christakos, P.; Petrellis, N.; Mousouliotis, P.; Keramidas, G.; Antonopoulos, C.P.; Voros, N. A High Performance and Robust FPGA Implementation of a Driver State Monitoring Application. Sensors 2023, 23, 6344.
Christakos, P.; Petrellis, N.; Mousouliotis, P.; Keramidas, G.; Antonopoulos, C.P.; Voros, N. A High Performance and Robust FPGA Implementation of a Driver State Monitoring Application. Sensors2023, 23, 6344.
Christakos, P.; Petrellis, N.; Mousouliotis, P.; Keramidas, G.; Antonopoulos, C.P.; Voros, N. A High Performance and Robust FPGA Implementation of a Driver State Monitoring Application. Sensors 2023, 23, 6344.
Abstract
A high performance Driver State Monitoring (DSM) application for the detection of driver drowsiness is presented in this paper. It relies on the usage of an Ensemble of Regression Trees (ERTs) machine learning method that aligns 68 facial landmarks. Special focus is given on the acceleration of the frame processing using reconfigurable hardware. Reducing the frame processing latency saves time that can be used to apply frame-to-frame facial shape coherency rules. False face detection and false shape estimations can be ignored for higher robustness and accuracy in the operation of the DSM application without reducing the frame processing rate that can reach 65 frames per second. The sensitivity and precision in yawning recognition can reach 93% and 97%, respectively. The implementation of the employed DSM algorithm in reconfigurable hardware is challenging since the kernel arguments require large data transfers and the degree of data reuse in the computational kernel is low. Due to this, unconventional hardware acceleration techniques have been employed that can also be useful for the acceleration of several other applications.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
,
,
