Abstract
We present a multi-camera 3D pedestrian detection method that does not need to train using data from the target scene. We estimate pedestrian location on the ground plane using a novel heuristic based on human body poses and person's bounding boxes from an off-the-shelf monocular detector. We then project these locations onto the world ground plane and fuse them with a new formulation of a clique cover problem. We also propose an optional step for exploiting pedestrian appearance during fusion by using a domain-generalizable person re-identification model. We evaluated the proposed approach on the challenging WILDTRACK dataset. It obtained a MODA of 0.569 and an F-score of 0.78, superior to state-of-the-art generalizable detection techniques.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 |
| Publisher | IEEE |
| Pages | 1232-1240 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781665448994 |
| DOIs | |
| Publication status | Published - 1 Sept 2021 |
| Event | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 - Virtual, Online, USA United States Duration: 19 Jun 2021 → 25 Jun 2021 |
Publication series
| Name | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
|---|---|
| ISSN (Print) | 2160-7508 |
| ISSN (Electronic) | 2160-7516 |
Conference
| Conference | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 |
|---|---|
| Country/Territory | USA United States |
| City | Virtual, Online |
| Period | 19/06/21 → 25/06/21 |
Funding
The authors would like to thank Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (process 425401/2018-9) for partially funding this research.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering