GridTrack: Detection and Tracking of Multiple Objects in Dynamic Occupancy Grids
| Autor: | Anshul Paigwar, David Sierra Gonzalez, Ozgur Erkent, Christian Laugier |
|---|---|
| Přispěvatelé: | Robots coopératifs et adaptés à la présence humaine en environnements dynamiques (CHROMA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Occupancy grid mapping
Computer science business.industry Track (disk drive) [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] tracking Tracking (particle physics) Grid Task (computing) occupancy grids Video tracking Fuse (electrical) Computer vision Artificial intelligence autonomous vehicles business Raw data |
| Zdroj: | ICCV 2021-International Conference on Computer Vision ICCV 2021-International Conference on Computer Vision, Oct 2021, Virtual Conference, Austria. pp.1-14 Lecture Notes in Computer Science ISBN: 9783030871550 ICVS ICVS 2021-International Conference on Vision Systems ICVS 2021-International Conference on Vision Systems, Oct 2021, Virtual Conference, Austria. pp.1-14, ⟨10.1007/978-3-030-87156-7_15⟩ |
| DOI: | 10.1007/978-3-030-87156-7_15⟩ |
| Popis: | International audience; Multiple Object Tracking is an important task for autonomous vehicles. However, it gets difficult to track objects when it is hard to detect them due to occlusion or distance to the sensors. We propose a method, "GridTrack", to overcome this difficulty. We fuse a dynamic occupancy grid map (DOGMa) with an object detector. DOGMa is obtained by applying a Bayesian filter on raw sensor data. This improves the tracking of the partially observed / unobserved objects with the help of the Bayesian filter on raw data, which has a powerful prediction capability. We develop a network to track the objects on the grid and fuse information from previous detections in this network. The experiments show that the multi-object tracking accuracy is high with the usage of the proposed method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|