Real-Time Edge Computing Framework for Drowsiness Detection in ITS using CLBP

Abstract

Today’s transportation systems suffer from a high number of accidents caused by drowsy driving, so strong automated detection systems are required for implementation in the Intelligent Transportation Systems (ITS). This paper introduces an analytical framework, divided into three stages to enhance the real-time detection of drowsiness at video level based on modules and provides the experimental results to analyze the proposed framework. To overcome the problems associated with computation-intensive deep learning-based methods, the proposed method involves employing Complete Local Binary Pattern (CLBP) texture descriptors, Dim: 512 with a greedy Pearson de-correlated feature selection that results in a trash-free subset of 58 texture bins (58 discriminative bins+auxiliary predictors), so that the features involved are largely reduced to 61. A comprehensive benchmark of 28 Classifier Presets including 9 Classifier families is performed on the NTHU Drowsy Driver Detection Dataset (357 video-derived observations) via five-fold cross validation. The best configuration, which is the Quadratic Support Vector Machine (SVM) trained on top 59 dimensions of normalised CLBP features, produces 92.42% accuracy and 88.1% dimensionality reduction with a weighted F1-score of 92.13%, which is 1.13 percentage points better than the weighted F1-score of the full-feature baseline. This solution offers an alternative to deep convolutional solutions for drowsiness detection in ITS that is both simple to interpret and able to use less computation.