Object Tracking
584 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
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
Simple Online and Realtime Tracking with a Deep Association Metric
nwojke/deep_sort
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Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms.
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
ifzhang/FairMOT
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Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency.
StrongSORT: Make DeepSORT Great Again
As a result, the construction of a good baseline for a fair comparison is essential.
Tracking without bells and whistles
phil-bergmann/tracking_wo_bnw
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ICCV 2019
Therefore, we motivate our approach as a new tracking paradigm and point out promising future research directions.
Towards Real-Time Multi-Object Tracking
Zhongdao/Towards-Realtime-MOT
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ECCV 2020
In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.
Center-based 3D Object Detection and Tracking
tianweiy/CenterPoint
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CVPR 2021
Three-dimensional objects are commonly represented as 3D boxes in a point-cloud.
Fully-Convolutional Siamese Networks for Object Tracking
The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object's appearance exclusively online, using as sole training data the video itself.
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
natdebru/OpenCV-Video-Label
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Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, its motion, and how it changes over time.
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
ifzhang/ByteTrack
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arXiv 2021
ByteTrack also achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks.
MOT16: A Benchmark for Multi-Object Tracking
PaddlePaddle/PaddleDetection
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Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods.
