Tracking Multiple Vehicles Constrained to a Road Network From a UAV with Sparse Visual Measurements
Multiple-target tracking algorithms generally operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field-of-view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road networ...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Robotics and AI |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2021.744185/full |
| _version_ | 1818729854401511424 |
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| author | Jared J. Moore Craig C. Bidstrup Cameron K. Peterson Randal W. Beard |
| author_facet | Jared J. Moore Craig C. Bidstrup Cameron K. Peterson Randal W. Beard |
| author_sort | Jared J. Moore |
| collection | DOAJ |
| description | Multiple-target tracking algorithms generally operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field-of-view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field-of-view. To address this problem, we propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves and then re-enters the UAV’s field-of-view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We implement two path planning controllers, receding horizon and deep reinforcement learning, and compare their ability to improve the certainty for multiple target location estimates. The controllers prioritize paths that reduce each target’s entropy. In addition, we develop an algorithm that computes the upper bound on the filter’s performance, thus facilitating an estimate of the number of UAVs needed to achieve a desired performance threshold. |
| first_indexed | 2024-12-17T22:52:30Z |
| format | Article |
| id | doaj.art-589fb2d580734d00838a7aea0561932b |
| institution | Directory Open Access Journal |
| issn | 2296-9144 |
| language | English |
| last_indexed | 2024-12-17T22:52:30Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj.art-589fb2d580734d00838a7aea0561932b2022-12-21T21:29:37ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442021-10-01810.3389/frobt.2021.744185744185Tracking Multiple Vehicles Constrained to a Road Network From a UAV with Sparse Visual MeasurementsJared J. MooreCraig C. BidstrupCameron K. PetersonRandal W. BeardMultiple-target tracking algorithms generally operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field-of-view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field-of-view. To address this problem, we propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves and then re-enters the UAV’s field-of-view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We implement two path planning controllers, receding horizon and deep reinforcement learning, and compare their ability to improve the certainty for multiple target location estimates. The controllers prioritize paths that reduce each target’s entropy. In addition, we develop an algorithm that computes the upper bound on the filter’s performance, thus facilitating an estimate of the number of UAVs needed to achieve a desired performance threshold.https://www.frontiersin.org/articles/10.3389/frobt.2021.744185/fullcooperative controlunmanned air vehiclesmultiple target trackingcomputer visionparticle filter |
| spellingShingle | Jared J. Moore Craig C. Bidstrup Cameron K. Peterson Randal W. Beard Tracking Multiple Vehicles Constrained to a Road Network From a UAV with Sparse Visual Measurements Frontiers in Robotics and AI cooperative control unmanned air vehicles multiple target tracking computer vision particle filter |
| title | Tracking Multiple Vehicles Constrained to a Road Network From a UAV with Sparse Visual Measurements |
| title_full | Tracking Multiple Vehicles Constrained to a Road Network From a UAV with Sparse Visual Measurements |
| title_fullStr | Tracking Multiple Vehicles Constrained to a Road Network From a UAV with Sparse Visual Measurements |
| title_full_unstemmed | Tracking Multiple Vehicles Constrained to a Road Network From a UAV with Sparse Visual Measurements |
| title_short | Tracking Multiple Vehicles Constrained to a Road Network From a UAV with Sparse Visual Measurements |
| title_sort | tracking multiple vehicles constrained to a road network from a uav with sparse visual measurements |
| topic | cooperative control unmanned air vehicles multiple target tracking computer vision particle filter |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2021.744185/full |
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