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• W3201042887 endingPage "194" @default.
• W3201042887 startingPage "180" @default.
• W3201042887 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." @default.
• W3201042887 created "2021-09-27" @default.
• W3201042887 creator A5003475208 @default.
• W3201042887 creator A5020071319 @default.
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• W3201042887 date "2021-01-01" @default.
• W3201042887 modified "2023-09-27" @default.
• W3201042887 title "GridTrack: Detection and Tracking of Multiple Objects in Dynamic Occupancy Grids" @default.
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• W3201042887 doi "https://doi.org/10.1007/978-3-030-87156-7_15" @default.
• W3201042887 hasPublicationYear "2021" @default.
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