Heterogeneous Learningを導入したDeep Convolutional Neural Networkによる運転手の状態のモニタリング
- 奥野薫子, 山下隆義, 福井宏, 山内悠嗣, 藤吉弘亘, 乘冨修蔵, 新浩治
- 精密工学会誌, vol. 83, No. 12, pp. 1101--1108, 2017
The autonomous driving at level 3 requires hand over the operation to driver when the system could not continue driving automatically. In order to response this requirement, the driver monitoring system is an important technology which estimates the posture and facial angle of driver. In addition, it has to detect the sitting on the driver seat as prior task. The conventional methods take time consuming because it extracts the hand craft features and learn classifiers with them for each task. In this paper, we introduce Heterogeneous Learning for simultaneous learning of multiple tasks by DCNN. It is able to share a feature extraction and output multiple tasks at the same time. We combine 2 tasks in a single DCNN that estimates body posture and facial angle. We also challenge the combination of driver sitting detection and body posture estimation. While the processing time of our method is 2.6 ms in GPU, the conventional DCNN is 3.4 ms. The performance of proposed methods with Heterogeneous Learning are comparable accuracy to DCNNs of single task. Our method is faster than conventional method with equivalent performance.