A labeled random finite set online multi-object tracker for video data
This paper proposes an online multi-object tracking algorithm for image observations using a top-down Bayesian formulation that seamlessly integrates state estimation, track management, handling of false positives, false negatives and occlusion into a single recursion. This is achieved by modeling t...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2019
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| Online Access: | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/74333 |