Name
Abstract | ||
|---|---|---|
The number of pedestrians walking the streets or gathered in public spaces is a valuable piece of information for shop owners, city governments, event organizers and many others. However, automatic counting that takes place day and night is challenging due to changing lighting conditions and the complexity of scenes with many people occluding one another. To address these challenges, this paper introduces the use of a stereo thermal camera setup for pedestrian counting. We investigate the reconstruction of 3D points in a pedestrian street with two thermal cameras and propose an algorithm for pedestrian counting based on clustering and tracking of the 3D point clouds. The method is tested on two five-minute video sequences captured at a public event with a moderate density of pedestrians and heavy occlusions. The counting performance is compared to the manually annotated ground truth and shows success rates of 95.4% and 99.1% for the two sequences. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3390/s16010062 | SENSORS |
Keywords | Field | DocType |
computer vision,pedestrian counting,occlusion,thermal,infrared,stereo,3D reconstruction,point cloud,tracking | Computer vision,Pedestrian,Simulation,Electronic engineering,Ground truth,Artificial intelligence,Engineering,Point cloud,Cluster analysis,Accident prevention,3D reconstruction | Journal |
Volume | Issue | ISSN |
16 | 1.0 | 1424-8220 |
Citations | PageRank | References |
8 | 0.56 | 43 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miklas S Kristoffersen | 1 | 12 | 3.06 |
| Jacob V Dueholm | 2 | 11 | 2.69 |
| Rikke Gade | 3 | 216 | 14.74 |
| Thomas Moeslund | 4 | 2721 | 186.08 |