Resource-aware video streaming (RAViS) framework for object detection system using deep learning algorithm

Video streams can come from various sources, such as surveillance cameras, live events, drones, and video-sharing platforms. Video stream mining is challenging due to the extensive resources needed to analyze and extract useful information from continuous video data streams. This situation could result in overwhelmed resources, which causes the system to stall. One of the ways to suffice the requirement is to provide larger resources, which leads to more costs. This research develops a data stream mining called the Resource-Aware Video Streaming (RAViS) framework to adapt to the limited resources (a Raspberry Pi) to run an object detection system using the YOLO algorithm. We validate the framework by capturing video streaming to simulate data streams. The video frames are processed using a deep-learning model to recognize the presence of a person(s) in a room. The RAViS framework adapts the object detection system to the availability of Raspberry Pi resources, such as CPU, RAM, and internal storage. The adaptation aims to increase the availability of resources to perform object detection of streamed video. The experimental results indicate that the RAViS framework can adapt the detection system to resource availability while maintaining accuracy. • A framework can ensure the availability of a computer with limited resources for running an object detection system using deep learning algorithms. • The framework constantly monitors the computer's memory, CPU, and storage, and provides feedback to the object detection system for adjusting its parameters to optimize resource utilization. • This approach enables the object detection system to operate continuously with the required resources, thus ensuring its accuracy and effectiveness.