Object Tracking
594 papers with code • 7 benchmarks • 62 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
These leaderboards are used to track progress in Object Tracking
Libraries
Use these libraries to find Object Tracking models and implementationsDatasets
KITTI
LaSOT
GOT-10k
MOTChallenge
VOT2018
Virtual KITTI
UAVDT
VOT2017
UA-DETRAC
Event-Camera Dataset
Subtasks
Most implemented papers
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors
DanceTrack/DanceTrack
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CVPR 2023
In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector.
Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline
wangxiao5791509/Single_Object_Tracking_Paper_List
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Tracking using bio-inspired event cameras has drawn more and more attention in recent years.
Long-term Frame-Event Visual Tracking: Benchmark Dataset and Baseline
event-ahu/felt_sot_benchmark
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Current event-/frame-event based trackers undergo evaluation on short-term tracking datasets, however, the tracking of real-world scenarios involves long-term tracking, and the performance of existing tracking algorithms in these scenarios remains unclear.
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
In this paper, we present a modular framework for tracking multiple objects (vehicles), capable of accepting object proposals from different sensor modalities (vision and range) and a variable number of sensors, to produce continuous object tracks.
Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification
longcw/MOTDT
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Online multi-object tracking is a fundamental problem in time-critical video analysis applications.
ATOM: Accurate Tracking by Overlap Maximization
visionml/pytracking
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CVPR 2019
We argue that this approach is fundamentally limited since target estimation is a complex task, requiring high-level knowledge about the object.
Fast Online Object Tracking and Segmentation: A Unifying Approach
foolwood/SiamMask
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CVPR 2019
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.
Intra-frame Object Tracking by Deblatting
rozumden/deblatting_python
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We propose a novel approach called Tracking by Deblatting based on the observation that motion blur is directly related to the intra-frame trajectory of an object.
TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications
Chang-Chia-Chi/TrackNet-Badminton-Tracking-tensorflow2
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The proposed heatmap-based deep learning network is trained to not only recognize the ball image from a single frame but also learn flying patterns from consecutive frames.
Argoverse: 3D Tracking and Forecasting with Rich Maps
In our baseline experiments, we illustrate how detailed map information such as lane direction, driveable area, and ground height improves the accuracy of 3D object tracking and motion forecasting.
