Object Tracking

343 papers with code • 6 benchmarks • 39 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 )

Libraries

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Most implemented papers

Simple Online and Realtime Tracking with a Deep Association Metric

nwojke/deep_sort 21 Mar 2017

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 4 Apr 2020

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.

Towards Real-Time Multi-Object Tracking

Zhongdao/Towards-Realtime-MOT ECCV 2020

In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.

Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects

natdebru/OpenCV-Video-Label 17 May 2017

Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, its motion, and how it changes over time.

Center-based 3D Object Detection and Tracking

tianweiy/CenterPoint CVPR 2021

Three-dimensional objects are commonly represented as 3D boxes in a point-cloud.

Tracking without bells and whistles

phil-bergmann/tracking_wo_bnw ICCV 2019

Therefore, we motivate our approach as a new tracking paradigm and point out promising future research directions.

MOT16: A Benchmark for Multi-Object Tracking

PaddlePaddle/PaddleDetection 2 Mar 2016

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.

Fully-Convolutional Siamese Networks for Object Tracking

zllrunning/SiameseX.PyTorch 30 Jun 2016

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.

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

ifzhang/ByteTrack arXiv 2021

ByteTrack also achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks.

DCFNet: Discriminant Correlation Filters Network for Visual Tracking

foolwood/DCFNet 13 Apr 2017

In this work, we present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously.