Visual Object Tracking

114 papers with code • 19 benchmarks • 18 datasets

Visual Object Tracking is an important research topic in computer vision, image understanding and pattern recognition. Given the initial state (centre location and scale) of a target in the first frame of a video sequence, the aim of Visual Object Tracking is to automatically obtain the states of the object in the subsequent video frames.

Source: Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking


Use these libraries to find Visual Object Tracking models and implementations

Most implemented papers

SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks

STVIR/pysot CVPR 2019

Moreover, we propose a new model architecture to perform depth-wise and layer-wise aggregations, which not only further improves the accuracy but also reduces the model size.

Deeper and Wider Siamese Networks for Real-Time Visual Tracking

researchmm/SiamDW CVPR 2019

Siamese networks have drawn great attention in visual tracking because of their balanced accuracy and speed.

Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective

xvjiarui/VFS ICCV 2021

To learn generalizable representation for correspondence in large-scale, a variety of self-supervised pretext tasks are proposed to explicitly perform object-level or patch-level similarity learning.

One-Shot Video Object Segmentation

kmaninis/OSVOS-PyTorch CVPR 2017

This paper tackles the task of semi-supervised video object segmentation, i. e., the separation of an object from the background in a video, given the mask of the first frame.

Discriminative Correlation Filter with Channel and Spatial Reliability

alanlukezic/csr-dcf CVPR 2017

Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance.

High Performance Visual Tracking With Siamese Region Proposal Network

foolwood/DaSiamRPN CVPR 2018

Visual object tracking has been a fundamental topic in recent years and many deep learning based trackers have achieved state-of-the-art performance on multiple benchmarks.

YouTube-VOS: Sequence-to-Sequence Video Object Segmentation

BehradToghi/ECCV_Youtube_VOS ECCV 2018

End-to-end sequential learning to explore spatial-temporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i. e., even the largest video segmentation dataset only contains 90 short video clips.

SiamVGG: Visual Tracking using Deeper Siamese Networks

leeyeehoo/SiamVGG 7 Feb 2019

It combines a Convolutional Neural Network (CNN) backbone and a cross-correlation operator, and takes advantage of the features from exemplary images for more accurate object tracking.

Staple: Complementary Learners for Real-Time Tracking

fengyang95/pyCFTrackers CVPR 2016

Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes.

ATOM: Accurate Tracking by Overlap Maximization

visionml/pytracking CVPR 2019

We argue that this approach is fundamentally limited since target estimation is a complex task, requiring high-level knowledge about the object.