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.
Libraries
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Latest papers
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.
Multiple Toddler Tracking in Indoor Videos
Multiple toddler tracking (MTT) involves identifying and differentiating toddlers in video footage.
Deep MDP: A Modular Framework for Multi-Object Tracking
This paper presents a fast and modular framework for Multi-Object Tracking (MOT) based on the Markov descision process (MDP) tracking-by-detection paradigm.
EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View
Most current approaches in multi-view tracking perform the detection and tracking task in each view and use graph-based approaches to perform the association of the pedestrian across each view.
Offline Tracking with Object Permanence
In this work, we propose an offline tracking model that focuses on occluded object tracks.