Single Object Tracking Research: A Survey
Not on the map. Why?
Abstract (arXiv)
Visual object tracking is an important task in computer vision, which has many real-world applications, e.g., video surveillance, visual navigation. Visual object tracking also has many challenges, e.g., object occlusion and deformation. To solve above problems and track the target accurately and efficiently, many tracking algorithms have emerged in recent years. This paper presents the rationale and representative works of two most popular tracking frameworks in past ten years, i.e., the corelation filter and Siamese network for object tracking. Then we present some deep learning based tracking methods categorized by different network structures. We also introduce some classical strategies for handling the challenges in tracking problem. Further, this paper detailedly present and compare the benchmarks and challenges for tracking, from which we summarize the development history and development trend of visual tracking. Focusing on the future development of object tracking, which we think would be applied in real-world scenes before some problems to be addressed, such as the problems in long-term tracking, low-power high-speed tracking and attack-robust tracking. In the future, the integration of multimodal data, e.g., the depth image, thermal image with traditional color image, will provide more solutions for visual tracking. Moreover, tracking task will go together with some other tasks, e.g., video object detection and segmentation.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 55 of its 119 references that are in the map. First posted on arXiv 25 Apr 2022.
Builds on
55 of 119 references.
References · 55
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,276
- 2016Fully-Convolutional Siamese Networks for Object Trackingcited by 568
- 2018GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wildcited by 475
- 2018SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networkscited by 446
- 2018LaSOT: A High-quality Benchmark for Large-scale Single Object Trackingcited by 429
- 2019Learning Discriminative Model Prediction for Trackingcited by 344
- 2018TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wildcited by 343
- 2018ATOM: Accurate Tracking by Overlap Maximizationcited by 285
- 2018Fast Online Object Tracking and Segmentation: A Unifying Approachcited by 272
- 2016ECO: Efficient Convolution Operators for Trackingcited by 239
- 2015Learning Multi-Domain Convolutional Neural Networks for Visual Trackingcited by 209
38 more
- 2020Siamese Box Adaptive Network for Visual Trackingcited by 189
- 2018Distractor-aware Siamese Networks for Visual Object Trackingcited by 187
- 2019SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelinescited by 177
- 2018Acquisition of Localization Confidence for Accurate Object Detectioncited by 176
- 2019Deeper and Wider Siamese Networks for Real-Time Visual Trackingcited by 156
- 2019SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Trackingcited by 148
- 2017Need for Speed: A Benchmark for Higher Frame Rate Object Trackingcited by 137
- 2018RGB-T Object Tracking:Benchmark and Baselinecited by 121
- 2020Know Your Surroundings: Exploiting Scene Information for Object Trackingcited by 119
- 2015A Novel Performance Evaluation Methodology for Single-Target Trackerscited by 113
- 2020Deformable Siamese Attention Networks for Visual Object Trackingcited by 97
- 2020High-Performance Long-Term Tracking with Meta-Updatercited by 80
- 2019D3S -- A Discriminative Single Shot Segmentation Trackercited by 69
- 2016Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Trackingcited by 66
- 2016Learning Spatially Regularized Correlation Filters for Visual Trackingcited by 66
- 2020Efficient Adversarial Attacks for Visual Object Trackingcited by 66
- 2017Learning Background-Aware Correlation Filters for Visual Trackingcited by 65
- 2018Siamese Cascaded Region Proposal Networks for Real-Time Visual Trackingcited by 65
- 2018Learning Spatial-Temporal Regularized Correlation Filters for Visual Trackingcited by 63
- 2019Multi-Modal Fusion for End-to-End RGB-T Trackingcited by 63
- 2020AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularizationcited by 62
- 2020Tracking by Instance Detection: A Meta-Learning Approachcited by 61
- 2015Staple: Complementary Learners for Real-Time Trackingcited by 60
- 2018Learning Dynamic Memory Networks for Object Trackingcited by 59
- 2018Long-term Tracking in the Wild: A Benchmarkcited by 55
- 2020State-Aware Tracker for Real-Time Video Object Segmentationcited by 54
- 2019Learning Aberrance Repressed Correlation Filters for Real-Time UAV Trackingcited by 47
- 2016Discriminative Scale Space Trackingcited by 46
- 2016Discriminative Correlation Filter with Channel and Spatial Reliabilitycited by 45
- 2019Dense Feature Aggregation and Pruning for RGBT Trackingcited by 40
- 2019'Skimming-Perusal' Tracking: A Framework for Real-Time and Robust Long-term Trackingcited by 34
- 2019CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmarkcited by 32
- 2020Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noisescited by 32
- 2017CREST: Convolutional Residual Learning for Visual Trackingcited by 29
- 2019SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Trackingcited by 29
- 2017Learning Policies for Adaptive Tracking with Deep Feature Cascadescited by 19
- 2019Visual Tracking via Dynamic Memory Networkscited by 11
- 2016SANet: Structure-Aware Network for Visual Trackingcited by 9
50 most cited of 55 All 55 (JSON)
Built on by
Citing papers per year: 2024: 1; 2025: 1.
2 papers.