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GridTrack: Detection and Tracking of Multiple Objects in Dynamic Occupancy Grids

Özgür Erkent 1 David Sierra Gonzalez 1 Anshul Paigwar 1 Christian Laugier 1 
1 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
Abstract : 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.
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https://hal.inria.fr/hal-03335282
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Submitted on : Monday, September 6, 2021 - 10:12:39 AM
Last modification on : Monday, May 16, 2022 - 4:46:03 PM
Long-term archiving on: : Tuesday, December 7, 2021 - 6:23:51 PM

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Özgür Erkent, David Sierra Gonzalez, Anshul Paigwar, Christian Laugier. GridTrack: Detection and Tracking of Multiple Objects in Dynamic Occupancy Grids. ICVS 2021 - International Conference on Vision Systems, Oct 2021, Virtual Conference, Austria. pp.1-14, ⟨10.1007/978-3-030-87156-7_15⟩. ⟨hal-03335282⟩

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