Object Tracking
572 papers with code • 7 benchmarks • 61 datasets
Object tracking is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment. State-of-the-art methods involve fusing data from RGB and event-based cameras to produce more reliable object tracking. CNN-based models using only RGB images as input are also effective. The most popular benchmark is OTB. There are several evaluation metrics specific to object tracking, including HOTA, MOTA, IDF1, and Track-mAP.
( Image credit: Towards-Realtime-MOT )
Benchmarks
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Libraries
Use these libraries to find Object Tracking models and implementationsDatasets
Subtasks
Latest papers with no code
TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint Prediction in Aerial Scenes
To address this issue, we introduce a novel task called Target-Aware Aerial Video Prediction, aiming to simultaneously predict future scenes and motion states of the target.
Middle Fusion and Multi-Stage, Multi-Form Prompts for Robust RGB-T Tracking
We propose M3PT, a novel RGB-T prompt tracking method that leverages middle fusion and multi-modal and multi-stage visual prompts to overcome these challenges.
Exploring Dynamic Transformer for Efficient Object Tracking
For instance, DyTrack obtains 64. 9% AUC on LaSOT with a speed of 256 fps.
Spike-NeRF: Neural Radiance Field Based On Spike Camera
As a neuromorphic sensor with high temporal resolution, spike cameras offer notable advantages over traditional cameras in high-speed vision applications such as high-speed optical estimation, depth estimation, and object tracking.
From Two Stream to One Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation
Due to the complementary nature of visible light and thermal in-frared modalities, object tracking based on the fusion of visible light images and thermal images (referred to as RGB-T tracking) has received increasing attention from researchers in recent years.
Spatio-Temporal Bi-directional Cross-frame Memory for Distractor Filtering Point Cloud Single Object Tracking
This integrates future and synthetic past frame memory to enhance the current memory, thereby improving the accuracy of iteration-based tracking.
Reasoning-Enhanced Object-Centric Learning for Videos
Object-centric learning aims to break down complex visual scenes into more manageable object representations, enhancing the understanding and reasoning abilities of machine learning systems toward the physical world.
CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking
Accurate detection and tracking of surrounding objects is essential to enable self-driving vehicles.
NetTrack: Tracking Highly Dynamic Objects with a Net
Most methods that solely depend on coarse-grained object cues, such as boxes and the overall appearance of the object, are susceptible to degradation due to distorted internal relationships of dynamic objects.
View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV
In this paper, we address the challenge of multi-object tracking (MOT) in moving Unmanned Aerial Vehicle (UAV) scenarios, where irregular flight trajectories, such as hovering, turning left/right, and moving up/down, lead to significantly greater complexity compared to fixed-camera MOT.