CIMOT3D：基于中文引导的单目视角下三维多目标跟踪研究

王荣; 胡海祥; 魏弘凯; 梁浩翔; 钱晓伟; 李凯飞; 郭柯宇; 宋翔宇; 孙士杰

doi:10.12263/DZXB.20250826

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CIMOT3D：基于中文引导的单目视角下三维多目标跟踪研究

学术论文 | 更新时间：2026-06-22

- CIMOT3D：基于中文引导的单目视角下三维多目标跟踪研究
- CIMOT3D: Chinese-Instruction-Based Monocular 3D Multi-Object Tracking
- 电子学报 2026年54卷第1期页码：102-114
- 作者机构：
  
  1.长安大学信息工程学院，陕西西安 710064
  2.长安大学电子与控制工程学院，陕西西安 710064
  3.长安大学数据科学与人工智能研究院，陕西西安 710064
- 作者简介：
  
  王荣男，2002年3月出生于山西省晋中市。现为长安大学信息工程学院硕士研究生。主要研究方向为计算机视觉、多模态融合与目标跟踪。E-mail: wr123@chd.edu.cn
  胡海祥男，1998年2月出生于山东省济宁市。现为长安大学信息工程学院硕士研究生。主要研究方向为计算机视觉、目标分割与目标跟踪。E-mail: 2024224045@chd.edu.cn
  魏弘凯男，2001年5月出生于福建省南平市。现为长安大学信息工程学院博士研究生。主要研究方向为三维计算机视觉、交通场景理解与医学图像处理。E-mail: hongkaiwei@chd.edu.cn
  梁浩翔男，1995年3月出生于陕西省西安市。现为长安大学电子与控制工程学院讲师、硕士生导师。主要研究方向为计算机视觉与智能交通、人工智能。E-mail: lhx@chd.edu.cn
  钱晓伟男，2002年4月出生于浙江省义乌市。现为长安大学信息工程学院硕士研究生。主要研究方向为三维计算机视觉。E-mail: xiaoweiqian@chd.edu.cn
  李凯飞男，2002年12月出生于山西省吕梁市。现为长安大学信息工程学院硕士研究生。主要研究方向为计算机视觉。E-mail: fei122813@chd.edu.cn
  郭柯宇男，1999年9月出生于贵州省黔南州。现为长安大学信息工程学院博士研究生。主要研究方向为计算机视觉与场景理解。E-mail: keyuguo@chd.edu.cn
  宋翔宇男，1991年3月出生于陕西省西安市。现为长安大学数据科学与人工智能研究院副教授、博士生导师。主要研究方向为交通异常事件视频语言大模型与多模态学习应用。E-mail: xiangyu.song@chd.edu.cn
  孙士杰男，1989年10月出生于河南省商丘市。现为长安大学数据科学与人工智能研究院副教授、国际生博士生导师。主要研究方向为多目标检测跟踪、交通三维重建与多目标位姿估计。E-mail: shijieSun@chd.edu.cn
- 基金信息：
  
  国家重点研发计划(2023YFB4301800);国家自然科学基金(62576050);国家资助博士后研究人员计划(GZC20241447);长安大学中央高校基本科研业务费专项资金(300102325101)
- DOI：10.12263/DZXB.20250826
  中图分类号： TP391.41;
- 收稿：2025-09-23，
  
  录用：2026-01-06，
  
  纸质出版：2026-01-25
- 稿件说明：
移动端阅览
王荣, 胡海祥, 魏弘凯, 等. CIMOT3D：基于中文引导的单目视角下三维多目标跟踪研究[J]. 电子学报, 2026, 54(01): 102-114.

WANG Rong, HU Haixiang, WEI Hongkai, et al. CIMOT3D: Chinese-Instruction-Based Monocular 3D Multi-Object Tracking[J]. Acta Electronica Sinica, 2026, 54(01): 102-114.
王荣, 胡海祥, 魏弘凯, 等. CIMOT3D：基于中文引导的单目视角下三维多目标跟踪研究[J]. 电子学报, 2026, 54(01): 102-114. DOI：10.12263/DZXB.20250826

WANG Rong, HU Haixiang, WEI Hongkai, et al. CIMOT3D: Chinese-Instruction-Based Monocular 3D Multi-Object Tracking[J]. Acta Electronica Sinica, 2026, 54(01): 102-114. DOI：10.12263/DZXB.20250826

摘要

自然语言描述驱动的目标跟踪通过解析符合人类表达习惯的语言描述，并将其与视觉信息融合，从而实现复杂环境中特定目标的精准识别与持续跟踪.然而，现有方法主要集中于二维场景或三维单目标跟踪，尚未扩展至三维多目标跟踪，缺乏将文本与三维视觉空间中多个候选目标进行特征对齐与关联建立的能力；此外，现有自然语言描述驱动三维目标跟踪任务在语言层面存在冗余问题，难以模拟人类基于灵活简练的指令对多个特定目标进行跟踪的能力.针对这些挑战，本文提出基于中文引导的单目视角下三维多目标跟踪新任务（Chinese-Instruction-based monocular 3D Multi-Object Tracking，CIMOT3D），并构建了含有5 562个视频序列的数据集CIMOT3D-5k，且所有序列均标注有符合人类表达习惯的中文描述.同时，本文设计了一种专用于该任务的神经网络模型CIMOT3D-SyncTracker（Chinese-Instruction-based monocular 3D Multi-Object tracking Synchronization Tracker），其框架由多模态特征提取器、视觉语言编解码器与检测跟踪模块三部分组成.相比于基线方法，本文方法在CIMOT3D-5k数据集上的跟踪准确率和身份一致性指标上分别提高了4.1和5.0个百分点，验证了其性能优势.本文拓展了视觉语言融合在三维多目标跟踪方向的研究深度，并为相关领域的后续探索提供了新的思路.

Abstract

Natural language-driven object tracking parses human-like language descriptions and fuses them with visual information to achieve accurate recognition and continuous tracking of specific targets in complex environments. However

existing methods focus on 2D tracking or 3D single-target tracking

and they have not been effectively extended to 3D multi-target tracking. They lack the capability to align text with multiple candidate targets in 3D visual space and to establish associations. In addition

existing natural language-driven 3D object tracking tasks suffer from redundancy in language descriptions

which makes it hard to track multiple specific targets using flexible and concise instructions as humans do. To address these challenges

this paper introduces a new task

chinese-instruction-based monocular 3D multi-object tracking (CIMOT3D). The paper also constructs a new dataset

CIMOT3D-5k

which contains 5 562 video sequences with human-like Chinese descriptions. Furthermore

this paper designs a neural network model chinese-instruction-based monocular 3D multi-object tracking synchronization tracker (CIMOT3D-SyncTracker) for this task

which consists of a multimodal feature extractor

a vision-language encoder-decoder

and a detection-tracking module. Compared with baseline methods

the proposed approach achieves an improvement of 4.1% in tracking accuracy and 5.0% in identity consistency metric on the CIMOT3D-5k dataset

verifying its performance advantage. This paper advances research on vision-language fusion in 3D multi-object tracking and offers new ideas for further exploration in related fields.

关键词

Keywords

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