Fusion of BIFFOA and Adaptive Two-Phase Mutation for Helmetless Motorcyclist Detection

Abstract

Road traffic injuries and deaths cause considerable economic losses to individuals, families, and nations as a whole. One of the strategies needed to curtail these fatalities is the surveillance of helmetless motorcyclists, which is carried out by developing an automatic detection system based on computer vision. Generally, this system consists of three subsystems, namely, moving object segmentation, motorcycle classification, and helmetless head detection. HOPG-LDB (Histogram of Oriented Phase and Gradient - Local Difference Binary) descriptor for this system produced good accuracy; however, it still has a drawback related to a large number of features. Based on these observations, this paper proposed an Adaptive Two-phase Mutation Binary Improved Fruit Fly Optimization Algorithm (ATMBIFFOA) to reduce the features. The ATMBIFFOA is a new feature selection algorithm that improved BIFFOA (Binary Improved Fruit Fly Optimization Algorithm) with an adaptive two-phase mutation algorithm. The BIFFOA produced good accuracy; however, weak in reducing feature dimension. The adaptive two-phase mutation algorithm was used to cover this weakness. The experiment results show that the proposed method can reduce the number of features and computation time effectively from BIFFOA. The proposed method produced motorcycle classification accuracy of 96.06% for the JSC1 dataset and 96.85% for the JSC2 dataset. As for helmetless head detection, the proposed method produced an average precision of 66.29% for the JSC1 dataset and 63.95% for the JSC2 dataset