Instant-level vehicle speed and traffic density estimation using deep neural network

Abstract

The growing network of highway video surveillance cameras generates an immense amount of data that proves tedious for manual analysis. Automated real-time analysis of such data may provide many solutions, including traffic monitoring, traffic incident detection, and smart-city planning. More specifically, assessing traffic speed and density is critical in determining dynamic traffic conditions and detecting slowdowns, traffic incidents, and traffic alerts. However, despite several advancements, there are numerous challenges in estimating vehicle speed and traffic density, which are integral parts of ITS. Some of these challenges include variations in road networks, illumination constraints, weather, structure occlusion, and vehicle user-driving behavior. To address these issues, this paper proposes a novel deep learning-based framework for instant-level vehicle speed and traffic flow density estimation to effectively harness the potential of existing large-scale highway surveillance cameras to assist in real-time traffic analysis. This is achieved using the state-of-the-art region-based Siamese MOT network, SiamMOT which detects and associates object instances for multi-object tracking (MOT), to accurately estimate instant level vehicle speed in live video feeds. The UA-DETRAC dataset is used to train the speed estimation model. Computer simulations show that the proposed framework a) allows the classifying of traffic density into light, medium, or heavy traffic flows, b) is robust to different types of road networks and illuminations without prior road information, and c) shows good performance when compared to current state-of-the-art methods using adequate performance metrics.