Summary of Rpee-heads: a Novel Benchmark For Pedestrian Head Detection in Crowd Videos, by Mohamad Abubaker et al.
RPEE-HEADS: A Novel Benchmark for Pedestrian Head Detection in Crowd Videos
by Mohamad Abubaker, Zubayda Alsadder, Hamed Abdelhaq, Maik Boltes, Ahmed Alia
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Railway Platforms and Event Entrances-Heads (RPEE-Heads) dataset is a novel, high-resolution, and accurately annotated resource for pedestrian head detection in crowded environments. The dataset consists of 109,913 annotated pedestrian heads across 1,886 images from 66 video recordings, with an average of 56.2 heads per image. Annotations include bounding boxes for visible head regions. Eight state-of-the-art object detection algorithms are evaluated using the RPEE-Heads dataset, and the results show that You Only Look Once v9 and Real-Time Detection Transformer outperform the other algorithms, achieving mean average precisions of 90.7% and 90.8%, with inference times of 11 and 14 milliseconds, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new database to help computers better recognize people’s heads in crowded areas like train stations and concert venues. They wanted to see how well existing computer programs could do this task. The database has over 100,000 images with labeled head regions. They tested eight different ways that computers can detect objects and found that two methods worked best. |
Keywords
» Artificial intelligence » Inference » Object detection » Transformer
