CEMO : Cloud Edge Architecture Development for a Multi Object Tracking

Due to increase of video surveillance situation, advance of autonomous driving technology, and development of artificial neural network, the multi-object tracking (MOT) has been attracted attention in the computer vision community. Moreover, the importance of multi-input processing and real-time analysis is increasing with the need for fast processing of many videos. Modern multi-object trackers use sequential processing to input continuous frames of video and derive tracking trajectories for all objects mainly on a single server. When performing deep learning with high computation on a single server, latency inevitably occurs. The latency is the main reason that the tracker cannot meet the real-time requirements. Removing the number of operations to reduce latency will immediately lead to poor performance of tracker. Cloud edge computing is the best way to meet the real-time distributed requirements because it can solve the data transmission delay problem of traditional cloud computing and effectively cooperate between edge devices. In this paper, we propose a new system structure called Cloud Edge Multi Object (CEMO) tracker for developing deep learning-based cloud-edge real-time video analysis applications. CEMO tracker is a container-based microservice structure that divides large application functions into small, independent units, and is a flexible architecture based on Kubernetes, a container orchestration platform. CEMO tracker, which can efficiently perform operations on simultaneous input, integrate the results and show them to users, is expected to solve multiple objects tracking problems efficiently through distributed cloud edge computing technology.