Case Study - Intelligent Vehicle Counter by Avenga | IoT ONE Digital Transformation Advisors

	Intelligent Vehicle Counter Avenga

Challenge	The Client is engaged in providing vehicle traffic counting for government services based on recorded video manual analysis. They considered applying an automation tool for vehicle detection and counting that would recognize a vehicle type according to certain categories among the general road traffic. This tool would optimize human efforts, eliminate errors, and speed up the workflow and consequently the results. Read More
About Customer	The client is a private company that deals with counting vehicles on the road. Read More
Customer Name	Not disclosed Read More
Solution	The Avenga team developed a semi-automated system for road traffic load estimations. The application is designed to expose a web-based user interface that allows traffic video processing, reporting, and validation. The data science team was responsible for developing the machine learning model based on computer vision technology. The solution consisted of detecting and classifying a vehicle on a video stream in multiple categories, tracking the vehicle through each frame, and eventually counting it using a deep learning approach. Read More Log in to view content
Contents

Challenge

The Client is engaged in providing vehicle traffic counting for government services based on recorded video manual analysis.

They considered applying an automation tool for vehicle detection and counting that would recognize a vehicle type according to certain categories among the general road traffic. This tool would optimize human efforts, eliminate errors, and speed up the workflow and consequently the results.

About Customer

The client is a private company that deals with counting vehicles on the road.

Customer Name

Not disclosed

Solution

The Avenga team developed a semi-automated system for road traffic load estimations. The application is designed to expose a web-based user interface that allows traffic video processing, reporting, and validation. The data science team was responsible for developing the machine learning model based on computer vision technology.

The solution consisted of detecting and classifying a vehicle on a video stream in multiple categories, tracking the vehicle through each frame, and eventually counting it using a deep learning approach.

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Contents

Overview

Intelligent Vehicle Counter