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
BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking
To utilize low-detection score bounding boxes in one-stage association, we propose to boost the confidence scores of two groups of detections: the detections we assume to correspond to the existing tracked object, and the detections we assume to correspond to a previously undetected object.
SFSORT: Scene Features-based Simple Online Real-Time Tracker
This paper introduces SFSORT, the world's fastest multi-object tracking system based on experiments conducted on MOT Challenge datasets.
DepthMOT: Depth Cues Lead to a Strong Multi-Object Tracker
Inspired by this, even though the bounding boxes of objects are close on the camera plane, we can differentiate them in the depth dimension, thereby establishing a 3D perception of the objects.
Self-Supervised Multi-Object Tracking with Path Consistency
In this paper, we propose a novel concept of path consistency to learn robust object matching without using manual object identity supervision.
Ego-Motion Aware Target Prediction Module for Robust Multi-Object Tracking
noyzzz/emap
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Conventional prediction methods in DBT utilize Kalman Filter(KF) to extrapolate the target location in the upcoming frames by supposing a constant velocity motion model.
Representation Alignment Contrastive Regularization for Multi-Object Tracking
Achieving high-performance in multi-object tracking algorithms heavily relies on modeling spatio-temporal relationships during the data association stage.
Multiple Object Tracking as ID Prediction
MCG-NJU/MOTIP
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In Multiple Object Tracking (MOT), tracking-by-detection methods have stood the test for a long time, which split the process into two parts according to the definition: object detection and association.
Fast-Poly: A Fast Polyhedral Framework For 3D Multi-Object Tracking
3D Multi-Object Tracking (MOT) captures stable and comprehensive motion states of surrounding obstacles, essential for robotic perception.
Lifting Multi-View Detection and Tracking to the Bird's Eye View
Taking advantage of multi-view aggregation presents a promising solution to tackle challenges such as occlusion and missed detection in multi-object tracking and detection.
Delving into the Trajectory Long-tail Distribution for Muti-object Tracking
chen-si-jia/Trajectory-Long-tail-Distribution-for-MOT
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In this study, we pioneer an exploration into the distribution patterns of tracking data and identify a pronounced long-tail distribution issue within existing MOT datasets.
