arXiv:2607.07322v1 Announce Type: cross Abstract: Automated crowd counting in Hajj video is difficult not because current models lack capacity, but because the footage violates the assumptions those models were built on: cameras observe the crowd from steep, near-vertical angles, individuals occlude one another extensively, and a single frame can contain well over a thousand people. Benchmarks that test crowd counting in such an environment are either private or not detailed per second. We revisit the HAJJv2 dataset and contribute HAJJv2-CrowdCount: per-second human-annotated crowd counts for
The proliferation of high-resolution video and the demand for accurate crowd analytics in challenging environments necessitate more robust AI benchmarks.
This new benchmark addresses a specific, difficult problem in computer vision, paving the way for more reliable and adaptable AI models in complex real-world scenarios.
AI models for crowd counting can now be rigorously tested against real-world data under extreme conditions, leading to improved performance in dense, occluded environments.
- · Computer Vision Researchers
- · Public Safety Agencies
- · Event Management Companies
- · AI models reliant on ideal, flat-angle camera assumptions
Improved accuracy in crowd density estimation in high-stakes environments.
Enhanced public safety and operational efficiency at large-scale events and crowded urban areas.
Potential for new AI applications in real-time crowd dynamics analysis and predictive modeling for spatial planning.
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Read at arXiv cs.AI