July 2, 2025

Summary of Multi-object Tracking Methods Combined with Deep Learning

Puntos clave

Multi-target tracking enhances target identity continuity in video sequences, improving applications ranging from vehicle tracking to action analysis.
Key findings demonstrate various tracking algorithms like joint detection and attention-based methods, crucial for optimizing tracking accuracy in diverse environments.
A comprehensive overview of algorithms is provided, emphasizing strengths and weaknesses across various implementations on the dataset MOT 17.
The findings suggest that advanced tracking methods may facilitate better performance in practical scenarios such as autonomous driving and video monitoring.

Resumen

The essence of the multi-target tracking (MOT) task is to identify and position multiple targets in the video sequence and ensure the continuity of the target identity in the time dimension, so as to produce the comprehensive path of each target in the whole video. Due to advancements in deep learning, multi-target tracking technology has undergone substantial enhancement, and is applicable to a variety of realistic application scenarios, such as autonomous driving, video surveillance, and action analysis, to meet diverse needs. On the basis of extensive literature research, according to the multi-target tracking algorithm framework, the current mainstream multi-target tracking algorithm is divided into multi-target tracking, joint detection, multi-target tracking and end-to-end multi-target tracking based on attention mechanism and introduce all kinds of representative algorithms in the framework of feature embedding. This paper examines the technical characteristics, advantages and disadvantages, and applicable environment of each type of algorithm in detail and shows the experimental results of each algorithm on the standard dataset MOT 17, so as to aid researchers in selecting suitable models for research.

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