3D MOT

MOT: Multi-Object Tracking

3D multi-object tracking follows objects through time, estimating their positions and preserving identities as they move, disappear behind obstacles, or enter a scene. Many systems detect objects in each frame and associate them using motion models such as Kalman filters, while learned methods use recurrent queries, transformers, appearance cues, or geometric features to make associations. Research spans autonomous-driving benchmarks such as KITTI, nuScenes, and Waymo, as well as multi-camera, cooperative-vehicle, robotics, and people-tracking settings. A central challenge is improving identity continuity and position accuracy under occlusion, sensor gaps, and changing environments while keeping systems fast, well-calibrated, and practical to deploy.

Latest papers

40 papers5 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

S3KF: Spherical State-Space Kalman Filtering for Panoramic 3D Multi-Object Tracking
Mar 29, 2026Zhongyuan Liu, Shaonan Yu, Jianping Li +53D MOT Kalman Filtering
CertaintyX · the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · ST Engineering, Singapore +1
V2U4Real: A Real-world Large-scale Dataset for Vehicle-to-UAV Cooperative Perception
Mar 26, 2026Weijia Li, Haoen Xiang, Tianxu Wang +4Autonomous Driving Datasets Cooperative Perception
Fujian Key Laboratory of Urban Intelligent Sensing and Computing, Xiamen University, China · Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China
RAM: Recover Any 3D Human Motion in-the-Wild
Mar 20, 2026Sen Jia, Ning Zhu, Jinqin Zhong +4Markerless Motion Capture 3D Human Pose Estimation
University of Washington · Anhui University · East China University of Science and Technology +1
Sparse3DTrack: Monocular 3D Object Tracking Using Sparse Supervision
Mar 18, 2026Nikhil Gosala, B. Ravi Kiran, Senthil Yogamani +1Visual Object Tracking 3D MOT
University of Freiburg, Germany · Qualcomm SARL France · Automated Driving, Qualcomm Technologies, Inc., USA
A Prediction-as-Perception Framework for 3D Object Detection
Mar 13, 2026Song Zhang, Haoyu Chen, Ruibo WangAutonomous Driving Perception 3D Object Detection
Z-one Technology Co., Ltd. 201 Anyan Road, Jiading, Shanghai, 201805, China
Fusion-Poly: A Polyhedral Framework Based on Spatial-Temporal Fusion for 3D Multi-Object Tracking
Mar 9, 2026Xian Wu, Yitao Wu, Xiaoyu Li +33D MOT Camera-LiDAR Fusion
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150006, China.
NOVA: Next-step Open-Vocabulary Autoregression for 3D Multi-Object Tracking in Autonomous Driving
Mar 6, 2026Kai Luo, Xu Wang, Rui Fan +1Visual Object Tracking Autonomous Driving
School of Artificial Intelligence and Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha 410082, China · College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China · State Key Laboratory of Intelligent Autonomous Systems, Tongji University, Shanghai 201804, China
Person Detection and Tracking from an Overhead Crane LiDAR
Mar 5, 2026Nilusha Jayawickrama, Henrik Toikka, Risto OjalaLidar-Based 3D Object Detection 3D Object Detection
School of Engineering, Aalto University, Espoo, Finland · School of Electrical Engineering, Aalto University, Espoo, Finland
SCATR: Mitigating New Instance Suppression in LiDAR-based Tracking-by-Attention via Second Chance Assignment and Track Query Dropout
Mar 2, 2026Brian Cheong, Letian Wang, Sandro Papais +1Multi-Object Tracking 3D MOT
University of Toronto
Towards Accurate State Estimation: Motion Dynamics Kalman Filter for 3D Multi-Object Tracking
May 12, 2025Mohamed Nagy, Naoufel Werghi, Bilal Hassan +2Kalman Filtering 3D MOT
Khalifa University, Abu Dhabi, UAE · New York University of Abu Dhabi

Mar 29, 2026J/K move · Enter open · S save

S3KF: Spherical State-Space Kalman Filtering for Panoramic 3D Multi-Object Tracking

Zhongyuan Liu, Shaonan Yu, Jianping Li, Pengfei Wan+4

CertaintyX · the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · ST Engineering, Singapore+1

3D MOT Kalman Filtering Camera-LiDAR Fusion

Open paper AskPDF arXiv

Panoramic multi-object tracking is important for industrial safety monitoring, wide-area robotic perception, and infrastructure-light deployment in large workspaces. In these settings, the sensing system must provide full-surround coverage, metric geometric cues, and stable target association under wide field-of-view distortion and occlusion. Existing image-plane trackers are tightly coupled to the camera projection and become unreliable in panoramic imagery, while conventional Euclidean 3D formulations introduce redundant directional parameters and do not naturally unify angular, scale, and depth estimation. In this paper, we present

\mathbf{S^3KF}

, a panoramic 3D multi-object tracking framework built on a motorized rotating LiDAR and a quad-fisheye camera rig. The key idea is a geometry-consistent state representation on the unit sphere

\mathbb{S}^2

, where object bearing is modeled by a two-degree-of-freedom tangent-plane parameterization and jointly estimated with box scale and depth dynamics. Based on this state, we derive an extended spherical Kalman filtering pipeline that fuses panoramic camera detections with LiDAR depth observations for multimodal tracking. We further establish a map-based ground-truth generation pipeline using wearable localization devices registered to a shared global LiDAR map, enabling quantitative evaluation without motion-capture infrastructure. Experiments on self-collected real-world sequences show decimeter-level planar tracking accuracy, improved identity continuity over a 2D panoramic baseline in dynamic scenes, and real-time onboard operation on a Jetson AGX Orin platform. These results indicate that the proposed framework is a practical solution for panoramic perception and industrial-scale multi-object tracking.The project page can be found at https://kafeiyin00.github.io/S3KF/.