mlsanityData to 7 Oct 2026

Ten Years of Pedestrian Detection, What Have We Learned?

arXiv 1411.4304 ↗First posted 16 Nov 2014cs.CVcited by 13

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Abstract (arXiv)

Paper-by-paper results make it easy to miss the forest for the trees.We analyse the remarkable progress of the last decade by discussing the main ideas explored in the 40+ detectors currently present in the Caltech pedestrian detection benchmark. We observe that there exist three families of approaches, all currently reaching similar detection quality. Based on our analysis, we study the complementarity of the most promising ideas by combining multiple published strategies. This new decision forest detector achieves the current best known performance on the challenging Caltech-USA dataset.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Nov 2014.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 3; 2023: 2; 2024: 3; 2025: 1; 2026: 1.

13 papers.

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  1. 20214D-Net for Learned Multi-Modal Alignmentcited by 44
  2. 2023Cross-Modality Proposal-guided Feature Mining for Unregistered RGB-Thermal Pedestrian Detectioncited by 12
  3. 2024A Culturally-Aware Benchmark for Person Re-Identification in Modest Attirecited by 6
  4. 2021Diagnosing Errors in Video Relation Detectorscited by 4
  5. 2021A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasetscited by 3
  6. 2022Toward Smart Doors: A Position Papercited by 2
  7. 2022Deep Learning Techniques for Visual Countingcited by 1
  8. 2022Crowd counting with segmentation attention convolutional neural networkcited by 0
  9. 2023Automated Sizing and Training of Efficient Deep Autoencoders using Second Order Algorithmscited by 0
  10. 2024A PST Algorithm for FPs Suppression in Two-stage CNN Detection Methodscited by 0
  11. 2024Real-Time Pedestrian Detection on IoT Edge Devices: A Lightweight Deep Learning Approachcited by 0
  12. 2025Automatic Labelling for Low-Light Pedestrian Detectioncited by 0
1 more
  1. 2026One-Shot Crowd Counting With Density Guidance For Scene Adaptationcited by 0