Multi-Object Tracking
197 papers with code • 19 benchmarks • 36 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.
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Latest papers
Multiple Object Tracking as ID Prediction
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
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.
EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous Driving
This paper introduces the task of Auditory Referring Multi-Object Tracking (AR-MOT), which dynamically tracks specific objects in a video sequence based on audio expressions and appears as a challenging problem in autonomous driving.
GBOT: Graph-Based 3D Object Tracking for Augmented Reality-Assisted Assembly Guidance
Augmented reality assembly guidance requires 6D object poses of target objects in real time.
iKUN: Speak to Trackers without Retraining
Referring multi-object tracking (RMOT) aims to track multiple objects based on input textual descriptions.
General Object Foundation Model for Images and Videos at Scale
We present GLEE in this work, an object-level foundation model for locating and identifying objects in images and videos.
UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation
In response to this, we introduce UCMCTrack, a novel motion model-based tracker robust to camera movements.
Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking
ByteTrack, a simple tracking algorithm, enables the simultaneous tracking of multiple objects by strategically incorporating detections with a low confidence threshold.