LiVeR: Lightweight Vehicle Detection and Classification in Real-Time

Abstract

Detection and classification of vehicles are very significant components in an Intelligent-Transportation System. Existing solutions not only use heavy-weight and costly equipment, but also largely depend on constant cloud (Internet) connectivity, as well as adequate uninterrupted power-supply. Such dependencies make these solutions fundamentally impractical considering the possible adversities of outdoor environment as well as requirement of correlated wide-area operation. For practical use, apart from being technically sound and accurate, a solution has to be lightweight, cost-effective, easy-to-install, flexible as well as supporting efficient time-correlated coverage over large area. In this work we propose an IoT-assisted strategy to fulfil all these goals together. We adopt a top-down approach where we first introduce a lightweight framework for time-correlated low-cost wide-area measurement and then reuse the concept for developing the individual measurement units. Our extensive outdoor measurement studies and trace-based simulation on the empirical data show about 98% accuracy in vehicle detection and upto 93% of accuracy in classification of the vehicles over moderately busy urban roads.

Real time two-wheeler recognition for Surveillance

“…It was shown that raising the convolution network's depth can be advantageous for computation and classification [22], [23] and [24]. For time-correlated real-time vehicle detection and classification over a large area, an ST-based framework has been presented [25].…”