Deep Learning-based Visual Multiple Object Tracking:A Review

Abstract

Multiple object tracking(MOT)aims to predict trajectories of all targets and maintain their identities from a given video sequence.In recent years,MOT has gained significant attention and become a hot topic in the field of computer vision due to its huge potential in academic research and practical application.Benefiting from the advancement of object detection and re-identification,the current approaches mainly split the MOT task into three subtasks:object detection,re-identification feature extraction,and data association.This idea has achieved remarkable success.However,maintaining robust tracking still remains challenging due to the factors such as occlusion and similar object interference in the tracking process.To meet the requirement of accurate,robust and real-time tracking in complex scenarios,further research and improvement of MOT algorithms are needed.Some review literature on MOT algorithms has been published.However,the existing literatures do not summarize the tracking approaches comprehensively and lack the latest research achievements.In this paper,the principle of MOT is firstly introduced,as well as the challenges in the tracking process.Then,the latest research achievements are summarized and analyzed.According to the tracking paradigm used to complete the three subtasks,the various algorithms are divided into separate detection and embedding,joint detection and embedding,and joint detection and tracking.The main characteristics of various tracking approaches are described.Afterward,the existing mainstream models are compared and analyzed on MOT challenge datasets.Finally,the future research directions are prospected by discussing the advantages and disadvantages of the current algorithms and their development trends.

Keywords

• multiple object tracking|computer vision|object detection|feature extraction|data association