Distributed Multi-Object Tracking under Limited Field of View Sensors
We consider the challenging problem of tracking multiple objects using a distributed network of sensors. In the practical setting of nodes with limited field of views (FoVs), computing power and communication resources, we develop a novel distributed multi-object tracking algorithm. To accomplish th...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2021
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1109/TSP.2021.3103125 http://hdl.handle.net/20.500.11937/91029 |
| _version_ | 1848765488376578048 |
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| author | Nguyen, Hoa Rezatofighi, H. Vo, Ba-Ngu Ranasinghe, D.C. |
| author_facet | Nguyen, Hoa Rezatofighi, H. Vo, Ba-Ngu Ranasinghe, D.C. |
| author_sort | Nguyen, Hoa |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We consider the challenging problem of tracking multiple objects using a distributed network of sensors. In the practical setting of nodes with limited field of views (FoVs), computing power and communication resources, we develop a novel distributed multi-object tracking algorithm. To accomplish this, we first formalise the concept of label consistency, determine a sufficient condition to achieve it and develop a novel label consensus approach that reduces label inconsistency caused by objects' movements from one node's limited FoV to another. Second, we develop a distributed multi-object fusion algorithm that fuses local multi-object state estimates instead of local multi-object densities. This algorithm: i) requires significantly less processing time than multi-object density fusion methods; ii) achieves better tracking accuracy by considering Optimal Sub-Pattern Assignment (OSPA) tracking errors over several scans rather than a single scan; iii) is agnostic to local multi-object tracking techniques, and only requires each node to provide a set of estimated tracks. Thus, it is not necessary to assume that the nodes maintain multi-object densities, and hence the fusion outcomes do not modify local multi-object densities. Numerical experiments demonstrate our proposed solution's real-time computational efficiency and accuracy compared to state-of-the-art solutions in challenging scenarios. |
| first_indexed | 2025-11-14T11:36:03Z |
| format | Journal Article |
| id | curtin-20.500.11937-91029 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:36:03Z |
| publishDate | 2021 |
| publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-910292023-05-22T08:05:14Z Distributed Multi-Object Tracking under Limited Field of View Sensors Nguyen, Hoa Rezatofighi, H. Vo, Ba-Ngu Ranasinghe, D.C. Science & Technology Technology Engineering, Electrical & Electronic Engineering Sensors Signal processing algorithms Sensor fusion Trajectory Bandwidth Australia Wireless sensor networks Multi-sensor multi-object tracking distributed multi-object tracking label consistency track consensus MULTI-BERNOULLI FILTER RANDOM FINITE SETS EFFICIENT IMPLEMENTATION DATA FUSION ASSIGNMENT ALGORITHMS ARCHITECTURES ASSOCIATION CONSENSUS AVERAGE cs.MA cs.MA cs.RO We consider the challenging problem of tracking multiple objects using a distributed network of sensors. In the practical setting of nodes with limited field of views (FoVs), computing power and communication resources, we develop a novel distributed multi-object tracking algorithm. To accomplish this, we first formalise the concept of label consistency, determine a sufficient condition to achieve it and develop a novel label consensus approach that reduces label inconsistency caused by objects' movements from one node's limited FoV to another. Second, we develop a distributed multi-object fusion algorithm that fuses local multi-object state estimates instead of local multi-object densities. This algorithm: i) requires significantly less processing time than multi-object density fusion methods; ii) achieves better tracking accuracy by considering Optimal Sub-Pattern Assignment (OSPA) tracking errors over several scans rather than a single scan; iii) is agnostic to local multi-object tracking techniques, and only requires each node to provide a set of estimated tracks. Thus, it is not necessary to assume that the nodes maintain multi-object densities, and hence the fusion outcomes do not modify local multi-object densities. Numerical experiments demonstrate our proposed solution's real-time computational efficiency and accuracy compared to state-of-the-art solutions in challenging scenarios. 2021 Journal Article http://hdl.handle.net/20.500.11937/91029 10.1109/TSP.2021.3103125 English http://dx.doi.org/10.1109/TSP.2021.3103125 http://purl.org/au-research/grants/arc/DP160104662 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC fulltext |
| spellingShingle | Science & Technology Technology Engineering, Electrical & Electronic Engineering Sensors Signal processing algorithms Sensor fusion Trajectory Bandwidth Australia Wireless sensor networks Multi-sensor multi-object tracking distributed multi-object tracking label consistency track consensus MULTI-BERNOULLI FILTER RANDOM FINITE SETS EFFICIENT IMPLEMENTATION DATA FUSION ASSIGNMENT ALGORITHMS ARCHITECTURES ASSOCIATION CONSENSUS AVERAGE cs.MA cs.MA cs.RO Nguyen, Hoa Rezatofighi, H. Vo, Ba-Ngu Ranasinghe, D.C. Distributed Multi-Object Tracking under Limited Field of View Sensors |
| title | Distributed Multi-Object Tracking under Limited Field of View Sensors |
| title_full | Distributed Multi-Object Tracking under Limited Field of View Sensors |
| title_fullStr | Distributed Multi-Object Tracking under Limited Field of View Sensors |
| title_full_unstemmed | Distributed Multi-Object Tracking under Limited Field of View Sensors |
| title_short | Distributed Multi-Object Tracking under Limited Field of View Sensors |
| title_sort | distributed multi-object tracking under limited field of view sensors |
| topic | Science & Technology Technology Engineering, Electrical & Electronic Engineering Sensors Signal processing algorithms Sensor fusion Trajectory Bandwidth Australia Wireless sensor networks Multi-sensor multi-object tracking distributed multi-object tracking label consistency track consensus MULTI-BERNOULLI FILTER RANDOM FINITE SETS EFFICIENT IMPLEMENTATION DATA FUSION ASSIGNMENT ALGORITHMS ARCHITECTURES ASSOCIATION CONSENSUS AVERAGE cs.MA cs.MA cs.RO |
| url | http://dx.doi.org/10.1109/TSP.2021.3103125 http://dx.doi.org/10.1109/TSP.2021.3103125 http://hdl.handle.net/20.500.11937/91029 |