Automatic Vehicle Counting Research Articles

A multilevel decision-making approach for road infrastructure management

ABSTRACT The design of effective road safety countermeasures requires a network diagnosis supported by data. Moreover, infrastructures need to be ready for the introduction of vehicle-to-infrastructure communications to support technologies, as truck platooning. Ascendi, a motorway concessionaire, developed an action plan to decrease crash frequency and casualties that rests in data recorded by automatic vehicle counting devices. To have a good network representation, new equipment will provide more data, thus enhancing the selection of countermeasures. We developed a multilevel decision-support approach to define equipment location. The process stages correspond to three levels of analysis: (1) clustering for road segment classification (network level); (2) quantification of the devices to install ensuring similar coverage (concession level); (3) device allocation according to geographical and cost criteria (segment level). An iterative and participatory process involving Ascendi resulted in a proposal for adding 43 devices to the existing 72, increasing the network coverage to 39%.

Implementasi Sistem Penghitung Kendaraan Otomatis Berbasis Computer Vision

The development of computer technology today is very helpful for humans in completing their work in various fields. One application of computer technology i.e., in the field of computer vision which has a very important role for object recognition. In this study, we designed a computer vision-based automatic vehicle counting system. The system that we created uses the MobileNetV2 Single Shot Multibox Detector (SSD) which is placed on the Raspberry Pi 4 to carry out the process of classifying cars and motorcycles and the raspberry pi 4 also functions as a system controller. This automatic vehicle counter system has been integrated between Raspberry Pi 4 and a mobile application on a smartphone where the smartphone functions to display information such as day, date, month, year and together with the number of cars and motorcycles. We tested this automatic vehicle counting system on steam services (car and motorcycle washing) for 3 days where 10 vehicles were collected every day. The test results show that the system is capable of detecting cars and motorcyles with an average accuracy rate of 46.6%.

Automatic Vehicle Counting and Tracking in Aerial Video Feeds using Cascade Region-based Convolutional Neural Networks and Feature Pyramid Networks

Unmanned aerial vehicles, or drones, are poised to solve many problems associated with data collection in complex urban environments. Drones are easy to deploy, have a great ability to move and explore the environment, and are relatively cheaper than other data collection methods. This study investigated the use of Cascade Region-based convolutional neural network (R-CNN) networks to enable automatic vehicle counting and tracking in aerial video streams.The presented technique combines feature pyramid networks and a Cascade R-CNN architecture to enable accurate detection and classification of vehicles.The paper discusses the implementation and evaluation of the detection and tracking techniques and highlights their advantages when they are used to collect traffic data.

Moving vehicle detection from video sequences for Traffic Surveillance System

In the current scenario, Intelligent Transportation Systems play a significant role in smart city platform. Automatic moving vehicle detection from video sequences is the core component of the automated traffic management system. Humans can easily detect and recognize objects from complex scenes in a flash. Translating that thought process to a machine, however, requires us to learn the art of object detection using computer vision algorithms. This paper solves the traffic issues of the urban areas with an intelligent automatic transportation system. This paper includes automatic vehicle counting with the help of blob analysis, background subtraction with the use of a dynamic autoregressive moving average model, identify the moving objects with the help of a Boundary block detection algorithm, and tracking the vehicle. This paper analyses the procedure of a video-based traffic congestion system and divides it into greying, binarisation, de-nosing, and moving target detection. The investigational results show that the planned system can provide useful information for traffic surveillance.

Efficient City-Wide Multi-Class Multi-Movement Vehicle Counting: A Survey

With the advent of accurate deep learning-based object detection methods, it is now possible to employ prevalent city-wide traffic and intersection cameras to derive actionable insights for improving traffic, road infrastructure, and transit. A crucial tool in signal timing planning is capturing accurate movement- and class-specific vehicle counts. To be useful in online intelligent transportation systems, methods designed for this task must not only be accurate in their counting, but should also be efficient. In this paper, we study the multi-class multi-movement vehicle counting problem, overview the state-of-the-art methods designed to solve this problem, and present a series of comprehensive experiments, using traffic footage with O(5) vehicles captured from 20 different vantage points and covering various lighting and weather conditions. Our survey aims to answer the question whether we are ready to leverage traffic cameras for real-time automatic vehicle counting. The results of our analysis show several promising approaches and identify areas where additional improvement is needed.

Automatic vehicle counting using background subtraction method on gray scale images and morphology operation

Traffic monitoring on road needs to be done, the counting of the number of vehicles passing the road is necessary. It is more emphasized for highway transportation management in order to prevent efforts. Therefore, it is necessary to develop a system that is able to counting the number of vehicles automatically. Video processing method is able to counting the number of vehicles automatically. This research has development a system of vehicle counting on toll road. This system includes processes of video acquisition, frame extraction, and image processing for each frame. Video acquisition is conducted in the morning, at noon, in the afternoon, and in the evening. This system employs of background subtraction and morphology methods on gray scale images for vehicle counting. The best vehicle counting results were obtained in the morning with a counting accuracy of 86.36 %, whereas the lowest accuracy was in the evening, at 21.43 %. Differences in morning and evening results are caused by different illumination in the morning and evening. This will cause the values in the image pixels to be different.

Automatic vehicle counting system for traffic monitoring

The article is dedicated to the presentation of a vision-based system for road vehicles counting and classification. The system is able to achieve counting with a very good accuracy even in difficult scenarios linked to occlusions and/or presence of shadows. The principle of the system is to use already installed cameras in road networks without any additional calibration procedure. We propose a robust segmentation algorithm that detects foreground pixels corresponding to moving vehicles. First, the approach models each pixel of the background with an adaptive Gaussian distribution. This model is coupled with a motion detection procedure which allows to correctly locate in space and time moving vehicles. The nature of trials carried out, including peak periods and various vehicle types, leads to an increase of occlusions between cars and between cars and trucks. A specific method for severe occlusion detection, based on the notion of solidity, has been carried out, and tested. Furthermore, the method developed in this work is capable of managing the shadows with high resolution. The related algorithm has been tested and compared to a classical method. Experimental results based on four large data-sets show that our method can count and classify vehicles in real-time with a high level of performance (more than 98%) under different environmental situations, thus performing better than the conventional inductive loop detectors.