mlsanityData to 14 Oct 2026

GUSOT: Green and Unsupervised Single Object Tracking for Long Video Sequences

arXiv 2207.07629 ↗First posted 15 Jul 2022cs.CVcited by 4

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Abstract (arXiv)

Supervised and unsupervised deep trackers that rely on deep learning technologies are popular in recent years. Yet, they demand high computational complexity and a high memory cost. A green unsupervised single-object tracker, called GUSOT, that aims at object tracking for long videos under a resource-constrained environment is proposed in this work. Built upon a baseline tracker, UHP-SOT++, which works well for short-term tracking, GUSOT contains two additional new modules: 1) lost object recovery, and 2) color-saliency-based shape proposal. They help resolve the tracking loss problem and offer a more flexible object proposal, respectively. Thus, they enable GUSOT to achieve higher tracking accuracy in the long run. We conduct experiments on the large-scale dataset LaSOT with long video sequences, and show that GUSOT offers a lightweight high-performance tracking solution that finds applications in mobile and edge computing platforms.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 26 of its 42 references that are in the map. First posted on arXiv 15 Jul 2022.

Builds on

26 of 42 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Fully-Convolutional Siamese Networks for Object Trackingcited by 568
  3. 2018SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networkscited by 446
  4. 2018LaSOT: A High-quality Benchmark for Large-scale Single Object Trackingcited by 429
  5. 2021Transformer Trackingcited by 412
  6. 2016ECO: Efficient Convolution Operators for Trackingcited by 239
  7. 2021Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Trackingcited by 220
  8. 2015Learning Multi-Domain Convolutional Neural Networks for Visual Trackingcited by 209
  9. 2018Distractor-aware Siamese Networks for Visual Object Trackingcited by 187
  10. 2014Return of the Devil in the Details: Delving Deep into Convolutional Netscited by 122
  11. 2016Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Trackingcited by 66
  12. 2018Learning Spatial-Temporal Regularized Correlation Filters for Visual Trackingcited by 63
14 more
  1. 2020AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularizationcited by 62
  2. 2015Staple: Complementary Learners for Real-Time Trackingcited by 60
  3. 2016Discriminative Scale Space Trackingcited by 46
  4. 2018Unveiling the Power of Deep Trackingcited by 41
  5. 2017CREST: Convolutional Residual Learning for Visual Trackingcited by 29
  6. 2020Unsupervised Deep Representation Learning for Real-Time Trackingcited by 29
  7. 2018Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Trackingcited by 25
  8. 2021Learning to Track Objects from Unlabeled Videoscited by 21
  9. 2021Unsupervised Visual Representation Learning by Tracking Patches in Videocited by 20
  10. 2022Unsupervised Learning of Accurate Siamese Trackingcited by 16
  11. 2018Deep Attentive Tracking via Reciprocative Learningcited by 14
  12. 2021UHP-SOT: An Unsupervised High-Performance Single Object Trackercited by 6
  13. 2019ROI Pooled Correlation Filters for Visual Trackingcited by 5
  14. 2021Unsupervised Lightweight Single Object Tracking with UHP-SOT++cited by 3

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Cited by year

Citing papers per year: 2022: 1; 2023: 3.

4 papers.

  1. 2022Acceleration of Subspace Learning Machine via Particle Swarm Optimization and Parallel Processingcited by 2
  2. 2023Green Steganalyzer: A Green Learning Approach to Image Steganalysiscited by 1
  3. 2023A Tiny Machine Learning Model for Point Cloud Object Classificationcited by 0
  4. 2023Unsupervised Green Object Tracker (GOT) without Offline Pre-trainingcited by 0