A Review of Multi‐Object Tracking in Recent Times
Suya Li, Hengyi Ren, Xin Xie, Ying Cao
Abstract
ABSTRACT Multi‐object tracking (MOT) is a fundamental problem in computer vision that involves tracing the trajectories of foreground targets throughout a video sequence while establishing correspondences for identical objects across frames. With the advancement of deep learning techniques, methods based on deep learning have significantly improved accuracy and efficiency in MOT. This paper reviews several recent deep learning‐based MOT methods and categorises them into three main groups: detection‐based, single‐object tracking (SOT)‐based, and segmentation‐based methods, according to their core technologies. Additionally, this paper discusses the metrics and datasets used for evaluating MOT performance, the challenges faced in the field, and future directions for research.