Recognition of driving postures by combined features and random subspace ensemble of multilayer perceptron classifiers

Human-centric driver assistance systems with integrated sensing, processing and networking aim to find solutions for traffic accidents and other relevant issues. The key technology for developing such a system is the capability of automatically understanding and characterizing driver behaviors. This paper proposes a novel driving posture recognition approach, which consists of an efficient combined feature extraction and a random subspace ensemble of multilayer perceptron classifiers. A Southeast University Driving Posture Database (SEU-DP Database) has been created for training and testing the proposed approach. The data set contains driver images of (1) grasping the steering wheel, (2) operating the shift lever, (3) eating a cake and (4) talking on a cellular phone. Combining spatial scale features and histogram-based features, holdout and cross-validation experiments on driving posture classification are conducted, comparatively. The experimental results indicate that the proposed combined feature extraction approach with random subspace ensemble of multilayer perceptron classifiers outperforms the two individual feature extraction approaches. The experiments also suggest that talking on a cellular phone is the most difficult posture in classification among the four predefined postures. Using the proposed approach, the classification accuracy on talking on a cellular phone is over 89 % in both holdout and cross-validation experiments. These results show the effectiveness of the proposed combined feature extraction approach and random subspace ensemble of multilayer perceptron classifiers in automatically understanding and characterizing driver behaviors toward human-centric driver assistance systems.