| Full text | |
| Author(s): |
Berri, Rafael
;
Osorio, Fernando
;
IEEE
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2018-01-01. |
| Abstract | |
In this work, a 3D vision system has been developed using a frontal Kinect v2 sensor to monitor the driver, enabling to recognize the use of a cell phone while driving, avoiding driving risks. In fact, when cars are driven by people on phone calls, it increases between 4 and 6 times the risk of crash. The Naturalistic Driver Behavior Dataset (NDBD) was created specifically for this work and it was used to test the proposed system. The proposed solution uses two analysis of the driver's hands positions, the Short-Term (ST) and Long-Term (LT) pattern recognition subsystems, thus it could detect the cell phone usage by the driver in hand-held situations. The system has 3 levels of alarm: no alarm, lowest alarm, and highest alarm. ST detects between no alarm or some level alarm. LT is responsible for determining the risk alarm level, low or high. The classifiers are based on Machine Learning and Artificial Neural Nets (ANN), furthermore, the values set to adjust input features, neuron activation functions, and network topology/training parameters were optimized and selected using a Genetic Algorithm. The best system performance results obtained in the experiments achieved 95% of accuracy in NDBD frames. For the ST classifier, it was used length periods of 5 frames and a window of 80 or 210 frames for LT. The best results achieved obtained only 1% of "no risk" situation having a wrong prediction (false positives with alarm activation), contributing to the driver comfort when he/she is using the system. (AU) | |
| FAPESP's process: | 13/25034-5 - The use of static driving simulator in supporting the studies of the design geometry and driving studies |
| Grantee: | Ana Paula Camargo Larocca |
| Support Opportunities: | Regular Research Grants |