mlsanityData to 14 Oct 2026

A Comprehensive Study of Object Tracking in Low-Light Environments

arXiv 2312.16250 ↗First posted 25 Dec 2023cs.CVcited by 8

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Abstract (arXiv)

Accurate object tracking in low-light environments is crucial, particularly in surveillance and ethology applications. However, achieving this is significantly challenging due to the poor quality of captured sequences. Factors such as noise, color imbalance, and low contrast contribute to these challenges. This paper presents a comprehensive study examining the impact of these distortions on automatic object trackers. Additionally, we propose a solution to enhance tracking performance by integrating denoising and low-light enhancement methods into the transformer-based object tracking system. Experimental results show that the proposed tracker, trained with low-light synthetic datasets, outperforms both the vanilla MixFormer and Siam R-CNN.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 24 of its 48 references that are in the map. First posted on arXiv 25 Dec 2023.

Builds on

24 of 48 references.

References · 24

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020End-to-End Object Detection with Transformerscited by 7,414
  3. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
  4. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,368
  5. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,358
  6. 2016Fully-Convolutional Siamese Networks for Object Trackingcited by 568
  7. 2021TrackFormer: Multi-Object Tracking with Transformerscited by 518
  8. 2017FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoisingcited by 510
  9. 2018GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wildcited by 475
  10. 2019EnlightenGAN: Deep Light Enhancement without Paired Supervisioncited by 466
  11. 2021MOTR: End-to-End Multiple-Object Tracking with Transformercited by 381
  12. 2019Learning Discriminative Model Prediction for Trackingcited by 344
12 more
  1. 2018Distractor-aware Siamese Networks for Visual Object Trackingcited by 187
  2. 2019Siam R-CNN: Visual Tracking by Re-Detectioncited by 174
  3. 2023Low-Light Image Enhancement with Wavelet-based Diffusion Modelscited by 127
  4. 2023Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Modelcited by 118
  5. 2023Global Structure-Aware Diffusion Process for Low-Light Image Enhancementcited by 92
  6. 2020Artificial Intelligence in the Creative Industries: A Reviewcited by 61
  7. 2022SUNet: Swin Transformer UNet for Image Denoisingcited by 44
  8. 2020Low Light Video Enhancement using Synthetic Data Produced with an Intermediate Domain Mappingcited by 26
  9. 2021Revisiting Temporal Alignment for Video Restorationcited by 24
  10. 2022HighlightNet: Highlighting Low-Light Potential Features for Real-Time UAV Trackingcited by 14
  11. 2021Contextual colorization and denoising for low-light ultra high resolution sequencescited by 10
  12. 2022A Topological Loss Function: Image Denoising on a Low-Light Datasetcited by 7

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 5.

8 papers.

  1. 2024A Spatio-temporal Aligned SUNet Model for Low-light Video Enhancementcited by 8
  2. 2024MouseSIS: A Frames-and-Events Dataset for Space-Time Instance Segmentation of Micecited by 5
  3. 2024Multi-Scale Denoising in the Feature Space for Low-Light Instance Segmentationcited by 4
  4. 2025Towards a General-Purpose Zero-Shot Synthetic Low-Light Image and Video Pipelinecited by 3
  5. 2025Advances in Artificial Intelligence: A Review for the Creative Industriescited by 2
  6. 2025CRTrack: Low-Light Semi-Supervised Multi-object Tracking Based on Consistency Regularizationcited by 1
  7. 2025Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniquescited by 1
  8. 2025ELVIS: Enhance Low-Light for Video Instance Segmentation in the Darkcited by 1