Event-Based Microservices With Apache Kafka Streams: A Real-Time Vehicle Detection System Based on Type, Color, and Speed Attributes

Seda Kul,Ahmet Sayar,Ali Sentas,Isabek Tashiev

doi:10.1109/access.2021.3085736

Seda Kul, Ahmet Sayar + Show 2 more

Open Access

https://doi.org/10.1109/access.2021.3085736

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Abstract

The work presented in this paper proposes a novel approach to tracking a specific vehicle over the video streams published by the collaborating traffic surveillance cameras. In recent years, smart, effective transportation systems and intelligent traffic management applications are among the topics that have been given importance by various institutions. Developing a scalable, fault-tolerant, and resilient traffic monitoring system that retrieves video chunks with the desired query is challenging. For these challenging problems, stream processing and data retrieval systems have been developed over the years. However, there are still existing shortcomings between users and retrieval systems. This paper investigates the problem of retrieving video chunks by key-value query based on publish/subscribe model. Thus, we propose a hybrid of an asynchronous and synchronous communication mechanism for the Event-Based Microservice framework. We aim to develop generic techniques for better utilization of existing platforms. In the proposed framework, (i) first of all, microservices detect vehicles and extract their type, color, and speed features, and stored them in the metadata repository. (ii) Microservices publish each feature as events (iii) Other microservices self-join subscribe to those events, which leads to more events being published by combing all the possibilities: type-color, type-speed, color-speed, and type-color-speed. Finally, (iv) the system visualizes the query result and system status in real-time. When the user has selected color or/and a type or/and a speed feature, the system will return the best-matched vehicles without re-processing the videos. Experimental results show that our proposed system filters messages in real-time and supports easy integration of new microservices with the existing system.

Highlights

Intelligent Transportation System (ITS) is becoming pervasive and actively used in recent years to increase traffic efficiency, decrease traffic congestion, and provide road safety
With a total of four vehicle classes, the results showed that support vector machine (SVM) performed better than RF with an accuracy of 96.26%
EXPERIMENTAL ANALYSIS OF VEHICLE DETECTION AND CLASSIFICATION Intersection over Union (IOU), Average Precision, Recall, and F-Score metrics used to measure the accuracy of the model on the test dataset

Summary

Introduction

Intelligent Transportation System (ITS) is becoming pervasive and actively used in recent years to increase traffic efficiency, decrease traffic congestion, and provide road safety. ITS uses communication technologies such as various sensors and cameras to produce useful information for operators. Implementing electronics, wireless and communication technologies on roads is costly. Surveillance camera systems such as Closed-circuit television (CCTV) and IP cameras have become widespread and actively used. The operators monitor surveillance camera feeds in real-time or recorded video. The need for access to up-to-date, accurate, and relevant data increases day by day.

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