TY - GEN
T1 - GridTrack
T2 - 13th International Conference on Computer Vision Systems, ICVS 2021
AU - Erkent, Özgür
AU - Gonzalez, David Sierra
AU - Paigwar, Anshul
AU - Laugier, Christian
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Autonomous vehicles
KW - Occupancy grids
KW - Tracking
UR - https://www.scopus.com/pages/publications/85115865117
U2 - 10.1007/978-3-030-87156-7_15
DO - 10.1007/978-3-030-87156-7_15
M3 - Conference contribution
AN - SCOPUS:85115865117
SN - 9783030871550
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 180
EP - 194
BT - Computer Vision Systems - 13th International Conference, ICVS 2021, Proceedings
A2 - Vincze, Markus
A2 - Patten, Timothy
A2 - Christensen, Henrik I
A2 - Nalpantidis, Lazaros
A2 - Liu, Ming
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 September 2021 through 24 September 2021
ER -