Referring MOT

MOT: Multi-Object Tracking

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12 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Refer-CityFlow and M3Track: A Benchmark Dataset and Reference Framework for Language-Driven Multi-Target Multi-Camera Tracking
Nov 6, 2026Phuong T. Nguyen, Hao Vo, Tam Bang +8Referring MOT Multi-Object Tracking
University of Tennessee at Chattanooga · University of Arkansas · RMIT International University Vietnam +1
DISTRACT: Degradation-Indexed Study of Textual Reference Across Competing Targets
Oct 20, 2026Dan Zlotnikov, Ohad Ben-ShaharMultimodal Visual Object Tracking VLM Robustness
Ben-Gurion University of the Negev, Israel
Referring Multi-Object Tracking in Moving-Camera Videos via Global Motion Compensation
Oct 3, 2026Hsin-Chen Pai, Jyun-Kai Wang, Yi-Cheng Peng +1Camera Motion Understanding Referring MOT
National Cheng Kung University, Tainan, Taiwan
YesTrack: Referring Multi-Object Tracking via MLLM-based Yes/No Verification
Sep 2, 2026Quansheng Hu, Qin Sun, Qiansen Dai +4Referring MOT Multi-Object Tracking
University of Electronic Science and Technology of China, Chengdu, China · Shenzhen Institute for Advanced Study, UESTC, Shenzhen, China
CD-RMOT-Bench: Benchmarking the Cross-Domain Referring Multi-Object Tracking
Jul 28, 2026Xiangqun Zhang, Likai Wang, Zekun Qian +2Synthetic-to-Real Domain Adaptation Referring MOT
School of Computer Science and Technology, Tianjin University, Tianjin, China · Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen, China
COAL: Counterfactual and Observation-Enhanced Alignment Learning for Discriminative Referring Multi-Object Tracking
May 14, 2026Shukun Jia, Shiyu Hu, Yipei Wang +3Referring MOT Counterfactual Learning
School of Automation, Southeast University, Nanjing, China · Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, China · School of Physical & Mathematical Sciences, Nanyang Technological University, Singapore +1
STORM: End-to-End Referring Multi-Object Tracking in Videos
Apr 12, 2026Zijia Lu, Jingru Yi, Jue Wang +4Referring MOT Multi-Object Tracking
Amazon
QTrack: Query-Driven Reasoning for Multi-modal MOT
Mar 14, 2026Tajamul Ashraf, Tavaheed Tariq, Sonia Yadav +4Referring MOT Multimodal Visual Object Tracking
King Abdullah University of Science and Technology (KAUST), Saudi Arabia · Gaash Research Lab, National Institute of Technology Srinagar, India · The University of Queensland, Australia
ORMOT: A Dataset and Framework for Omnidirectional Referring Multi-Object Tracking
Mar 5, 2026Zihan Zhou, Sijia Chen, Yanqiu Yu +2Referring MOT Visual Object Tracking
National Key Laboratory of Science and Technology on Multi-spectral Information Processing, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
RT-RMOT: A Dataset and Framework for RGB-Thermal Referring Multi-Object Tracking
Feb 25, 2026Yanqiu Yu, Zhifan Jin, Sijia Chen +4Referring MOT Visual Object Tracking
Huazhong University of Science and Technology, Wuhan, Hubei, China · South-Central Minzu University, Wuhan, Hubei, China
DRMOT: A Dataset and Framework for RGBD Referring Multi-Object Tracking
Feb 4, 2026Sijia Chen, Shaofeng Liang, Lijuan Ma +7Referring MOT Multi-Object Tracking
Huazhong University of Science and Technology · South-Central Minzu University
Referring Multi-Object Tracking with Diverse Dynamic Expressions
Jun 7, 2024Yani Zhang, Dongming Wu, Wencheng Han +3Referring MOT Multi-Object Tracking
School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan 430072, China. · Multimedia Lab (MMLab), Department of Information Engineering, the Chinese University of Hong Kong, Hong Kong, China. · State Key Laboratory of Internet of Things for Smart City, Department of Computer and Information Science, University of Macau, Macau, China. +1

Nov 6, 2026J/K move · Enter open · S save

Refer-CityFlow and M3Track: A Benchmark Dataset and Reference Framework for Language-Driven Multi-Target Multi-Camera Tracking

Phuong T. Nguyen, Hao Vo, Tam Bang, Hoang K. Tran+7

University of Tennessee at Chattanooga · University of Arkansas · RMIT International University Vietnam+1

Referring MOT Multi-Object Tracking

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Referring multi-object tracking enables users to specify targets through natural language, but existing Refer-MOT benchmarks primarily focus on single-camera settings. We formalize Referring Multi-Target Multi-Camera Tracking (Refer-MTMCT), where free-form queries specify identity-consistent trajectories to be recovered across a network of sequential, sparse-overlap cameras. We introduce Refer-CityFlow, a benchmark built on CityFlow's global cross-camera identity annotations and augmented with language queries requiring joint language grounding, cross-camera association, and spatio-temporal reasoning. Refer-CityFlow covers 96 videos from 40 roadside cameras with 660+ identity-consistent cross-camera trajectories, 4,362 trajectory-level descriptions, and 7,699 queries, including 1,699 real-user queries collected from 50 participants. Reference experiments on the four-camera Scenario 02 show that single-camera referring trackers reach 14.95-18.28 HOTA on real-user queries, while the evaluated cross-view method achieves about 0.05 HOTA in direct transfer. To establish reference performance, we provide M3Track, a modular multimodal reference framework in which a large language model (LLM)-based agent orchestrates interchangeable detection, tracking, and cross-camera association modules. M3Track reaches 44.10 HOTA and 65.40 IDF1 on the same real-user query set. The project repository is available at https://github.com/phuongnguyen194/Refer-CityFlow-and--M3Track.