Multi-Object Tracking
204 papers with code • 19 benchmarks • 37 datasets
Multi-Object Tracking is a task in computer vision that involves detecting and tracking multiple objects within a video sequence. The goal is to identify and locate objects of interest in each frame and then associate them across frames to keep track of their movements over time. This task is challenging due to factors such as occlusion, motion blur, and changes in object appearance, and is typically solved using algorithms that integrate object detection and data association techniques.
Benchmarks
These leaderboards are used to track progress in Multi-Object Tracking
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Libraries
Use these libraries to find Multi-Object Tracking models and implementations
KITTI
BDD100K
MOT17
MOTChallenge
MOT16
Virtual KITTI
UAVDT
MOT15
DanceTrack
Wildtrack
Subtasks
Latest papers with no code
Multi-Object Tracking with Camera-LiDAR Fusion for Autonomous Driving
This paper presents a novel multi-modal Multi-Object Tracking (MOT) algorithm for self-driving cars that combines camera and LiDAR data.
DeconfuseTrack:Dealing with Confusion for Multi-Object Tracking
Moreover, DeconfuseTrack achieves state-of-the-art performance on the MOT17 and MOT20 test sets, significantly outperforms the baseline tracker ByteTrack in metrics such as HOTA, IDF1, AssA.
Joint Spatial-Temporal Calibration for Camera and Global Pose Sensor
Yet, to work optimally, these functionalities require having accurate and reliable spatial-temporal calibration parameters between the camera and the global pose sensor.
Multi-Object Tracking by Hierarchical Visual Representations
We propose a new visual hierarchical representation paradigm for multi-object tracking.
MapTrack: Tracking in the Map
The prediction map determines whether an object is in a crowd, and we prioritize state estimations over observations when severe deformation of observations occurs, accomplished through the covariance adaptive Kalman filter.
BronchoTrack: Airway Lumen Tracking for Branch-Level Bronchoscopic Localization
Localizing the bronchoscope in real time is essential for ensuring intervention quality.
UncertaintyTrack: Exploiting Detection and Localization Uncertainty in Multi-Object Tracking
Multi-object tracking (MOT) methods have seen a significant boost in performance recently, due to strong interest from the research community and steadily improving object detection methods.
Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking
We further propose a new cost function for associating observations with tracks.
AM-SORT: Adaptable Motion Predictor with Historical Trajectory Embedding for Multi-Object Tracking
AM-SORT is a novel extension of the SORT-series trackers that supersedes the Kalman Filter with the transformer architecture as a motion predictor.
MoD2T:Model-Data-Driven Motion-Static Object Tracking Method
This novel performance metric is designed to measure the accuracy of motion state classification, providing a comprehensive evaluation of MoD2T's performance.
