Computer vision approach for the classification of bike type (motorized versus non-motorized) during busy traffic in the city of Shanghai

This article describes a novel approach for the binary classification of two-wheeler road users in a dense mixed traffic intersection. The classification is a supervised procedure to differentiate between motorized and non-motorized (human-powered) bikes. Road users were first detected and tracked using object recognition methods. Classification features were then selected from the collected trajectories. The features include maximum speed, cadence frequency in addition to acceleration-based parameters. Experiments were conducted on a video data set from Shanghai, China, where cyclists as well as motorcycles tend to share the main road facilities. A sensitivity analysis was performed to assess the quality of the selected features in improving the accuracy of the classification. A performance analysis demonstrated the robustness of the proposed classification method with a correct classification rate of up to 93%. This research contributes to the literature of automated data collection and can benefit the applications in many transportation-related fields such as shared space facility planning, simulation models for two-wheelers, and behavior analysis and road safety studies. Copyright (c) 2015 John Wiley & Sons, Ltd.