Motion-based Vehicle Speed Measurement for Intelligent Transportation Systems

Abstract

Video-based vehicle speed measurement systems are known as effective applications for Intelligent Transportation Systems (ITS) due to their great development capabilities and low costs. These systems utilize camera outputs to apply video processing techniques and extract the desired information. This paper presents a new vehicle speed measurement approach based on motion detection. Contrary to featurebased methods that need visual features of the vehicles like license-plate or windshield, the proposed method is able to estimate vehicle's speed by analyzing its motion parameters inside a pre-defined Region of Interest (ROI) with specified dimensions. This capability provides realtime computing and performs better than feature-based approaches. The proposed method consists of three primary modules including vehicle detection, tracking, and speed measurement. Each moving object is detected as it enters the ROI by the means of Mixture-of-Gaussian background subtraction method. Then by applying morphology transforms, the distinct parts of these objects turn into unified filled shapes and some defined filtration functions leave behind only the objects with the highest possibility of being a vehicle. Detected vehicles are then tracked using blob tracking algorithm and their displacement among sequential frames are calculated for final speed measurement module. The outputs of the system include the vehicle's image, its corresponding speed, and detection time. Experimental results show that the proposed approach has an acceptable accuracy in comparison with current speed measurement systems.

Vision‐based vehicle speed estimation: A survey

“…Second, those methods focused on tracking the centroid of the blob or the bounding box of the vehicle [25, 34, 35, 40, 48, 50, 52, 56, 65, 72, 73, 75, 83, 86, 95, 103, 106, 111, 118, 121, 122]. Finally, those approaches that focus on tracking the entire vehicle [15, 16, 18, 26–30, 32, 36, 42–44, 47, 54, 57, 63, 78, 81, 84, 89, 91, 98, 102, 104, 105, 108, 109, 111, 113, 114, 120, 125, 128, 129], or a specific part of it (such as the license plate [41, 49, 60, 66, 68, 70, 76, 77, 80, 82, 94, 100, 125]).…”

“…In a few cases, the problem of detecting the speed of vehicles is posed as a problem of detecting the speed of traffic on a segment of the road, i.e. directly obtaining an average road speed value [12,17,20,23,32,34,38,40,42,51,53,54,58,61,69,77,79,80,81,84,85,94,102,105,110,113,121]. However, in most cases the detection is carried out at the level of individual vehicles.…”

“…The most common approach to perform vehicle detection is based on background subtraction [ 121], followed by some morphological operations and a blob analysis method. Different methodologies are used to perform background subtraction, including gray-and color-based approaches, Gaussian Mixture Models [23,61,84,102], adaptive background modeling [51,111], etc. In some cases, frame-by-frame or background subtraction is combined with some feature detector (e.g., edges, corners, or KLT features) [40,48,50,71,110].…”

Implementasi Mobilenetv2 Dan Frame Difference Untuk Penentuan Kecepatan Kendaraan

“…Sistem pengukuran kecepatan berbasis video diketahui sebagai implementasi Intelligent Transportation System (ITS) yang bagus dikembangkan karena kapabilitas dan biaya murah. Penentuan pelanggaran kecepatan kendaraan juga dapat dikembangkan melalui deteksi gerakan (Tourani, Shahbrami, Akoushideh, Khazaee, & Suen, 2019). Kemunculan metode-metode pengenalan pola, bentuk benda, dan pengukuran berbasis pengolahan citra telah dimanfaatkan pada beragam aplikasi kehidupan (Reddy, Priya, & Neelima, 2015).…”

The Application of Virtual Reality Technology on Intelligent Traffic Construction and Decision Support in Smart Cities

“…However, they only aimed to improve public transport and could not fully cover road traffic [4]. Tourani proposed a decision support model for urban traffic planning based on traffic big data, which is divided into three stages: the location data of taxi passengers entering and leaving, the definition of alternative public transport routes, and the comparison of alternative schemes to scientifically calculate the public transport lines that the government should increase [5].…”