Spotlights

[Research News] Can Smart Cameras Improve Evacuations? A New Approach to Smarter Crowd Mapping

A new method for computational reconstruction of depth in sparse remote-sensing images to improve real-time crowd monitoring during disaster responses

Accurate crowd monitoring is crucial for guiding emergency evacuations. Non-repetitive scanning LiDAR systems are affordable and offer wide coverage, but their sparse and discontinuous depth data limit practical use. A research team led by Professor Nobutaka Tsujiuchi at Doshisha University, including Zixuan Zhang, a Ph.D. student, has introduced a color-guided depth completion method to reconstruct missing 3D information from sparse LiDAR data. The team also created a simulated dataset to support the evaluation of future depth reconstruction approaches.

Reference
Zixuan Zhang, Nobutaka Tsujiuchi, Akihito Ito, Hirosuke Horii, RGB-guided sparse depth completion for non-repetitive scanning LiDAR in crowd dynamics analysis, Transportation Research Interdisciplinary Perspectives,　Volume 36, March 2026, 101879
DOI:  https://doi.org/10.1016/j.trip.2026.101879

For more details, please see the website of Organization for Research Initiatives and Development, Doshisha University. 
https://research.doshisha.ac.jp/news/news-detail-92/

This achievement has also been featured in the “EurekAlert!.
URL　https://www.eurekalert.org/news-releases/1117884

Image Title: Computational depth-recovery of sparse images from non-repetitive LiDAR to enhance the accuracy of crowd-monitoring
Image Caption: Sparse and irregular depth measurements from non-repetitive LiDAR (left) and the reconstructed dense depth map using the proposed RGB (red, green, blue)-guided completion framework (right).
Image Credit: Zixuan Zhang from Doshisha University, Japan
Image license type: Original content
License restriction: Credit must be given to the creator.