A hybrid vision system for detecting use of mobile phones while driving
In this work, a vision system has been developed using a frontal camera to monitor the driver, enabling to recognize the use of a cell phone while driving. It is estimated that 80% of car crashes and 65% of near collisions involved drivers who were inattentive in traffic for three seconds before the event. Five videos in real environments were generated to test the proposed system. The solution is a hybrid system and that uses a pattern recognition system (PR) for classification and a movement detection system (MD) for choosing the PR parameters at the end of each period of 3 seconds. The PR parameters are the threshold (frames identified as a cell phone use) and classifier selection. The classifiers are based on ANN, furthermore, the value of constants in neuron activation function and network training parameters were adopted with a genetic algorithm. Experimentally, it was established that when the movement indicates a possible use of the cell phone, the threshold 60% and an MLP/Gaussian classifier with seven neurons in intermediate layer are suitable; otherwise, a threshold of 85% and MLP/Gaussian with two neurons in intermediate layer for classification are used. The average accuracy achieved was 91.68% in real environment scenes.
2016 International Joint Conference on Neural Networks (IJCNN)