Journal
ACM COMPUTING SURVEYS
Volume 53, Issue 4, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3394659
Keywords
Multi-object tracking; data association; machine learning; deep learning
Categories
Funding
- National Science Foundation [1446813]
- Florida DOT [BVD31977-45]
- Paul and Heidi Brown preeminent professorship at ISE, University of Florida
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1446813] Funding Source: National Science Foundation
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Data association is a key step within the multi-object tracking pipeline that is notoriously challenging due to its combinatorial nature. A popular and general way to formulate data association is as the NP-hard multidimensional assignment problem. Over the past few years, data-driven approaches to assignment have become increasingly prevalent as these techniques have started to mature. We focus this survey solely on learning algorithms for the assignment step of multi-object tracking, and we attempt to unify various methods by highlighting their connections to linear assignment and to the multi-dimensional assignment problem. First, we review probabilistic and end-to-end optimization approaches to data association, followed by methods that learn association affinities from data. We then compare the performance of the methods presented in this survey and conclude by discussing future research directions.
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