Computing‐efficient video analytics for nighttime traffic sensing

Abstract

AbstractThe training workflow of neural networks can be quite complex, potentially time‐consuming, and require specific hardware to accomplish operation needs. This study presents a novel analytical video‐based approach for vehicle tracking and vehicle volume estimation at nighttime using a monocular traffic surveillance camera installed over the road. To build this approach, we employ computer vision‐based algorithms to detect vehicle objects, perform vehicle tracking, and vehicle counting in a predefined detection zone. To address low‐illumination conditions, we adapt and employ image noise reduction techniques, image binary conversion, image projective transformation, and a set of heuristic reasoning rules to extract the headlights of each vehicle, pair them belonging to the same vehicle, and track moving candidate vehicle objects continuously across a sequence of video frames. The robustness of the proposed method was tested in various scenarios and environmental conditions using a publicly available vehicle dataset as well as own labeled video data.