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

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.