dc.description.abstract | Driver abnormal behavior detection is a hot issue that has been paid attention in recent years, the system detects the driver′s facial posture and other limbs to determine the current driver′s state. If abnormal behaviors such as distraction and drowsy driving have been detected, the system will make a warning sound to remind the driver to reduce the potential risks of car accident casualties. Since the vehicle conditions in real scenes change a lot, the detection system must provide both "high detection accuracy" and "capable of real time detection" abilities.
In recent years, deep learning has achieved considerable success in computer vision field. So far, there have been many papers using computer vision techniques to solve driver abnormal behavior detection issue. However, in the existing literature methods, both "accuracy" and "speed" cannot achieve good results at the same time. In this paper, we based on the method that combining 2D and 3D convolutional neural network from action recognition literature[15], this network can extract both static and dynamic features well. We regard this network as a shared network, and learn drowsiness, nodding, yawning and smoking classes simultaneously, as well as other regularization techniques like batch normalization, pre-training, data augmentations, etc. to further improve performance. In addition, we also design online video action prediction algorithm based on [15], this algorithm not only can handle online prediction problems efficiently, but also can extract longer-range temporal structure, further improving action detection accuracy.
In the experiment part, we explore the effects of pre-training, data augmentations, network complexity, the size of sampling frames, and different network architecture methods in terms of model performance. We confirm that we can gain significant accuracy improvements by applying pre-training and data augmentations, and if more types of data augmentations are used, the accuracy can be further improved again. Furthermore, according to the experiments of sampling frames and different network architecture methods, we also confirm that our model can not only achieve real time inference speed but also outperform 2D CNN and 3D CNN methods in terms of both accuracy and model size. | en_US |