Survey of amplitude‐aided multi‐target tracking methods
Author(s) -
Bae SeungHwan
Publication year - 2019
Publication title -
iet radar, sonar and navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 82
eISSN - 1751-8792
pISSN - 1751-8784
DOI - 10.1049/iet-rsn.2018.5064
Subject(s) - computer science , amplitude , tracking (education) , feature (linguistics) , artificial intelligence , stability (learning theory) , pattern recognition (psychology) , computer vision , data mining , machine learning , optics , physics , psychology , pedagogy , linguistics , philosophy
An amplitude‐aided multi‐target tracking (MTT) exploits amplitude as well as spatial features for MTT. Compared to MTT using a spatial feature only, it can maintain its performance even in densely cluttered environment because more accurate association is achieved by using both features for likelihood evaluation between tracks and measurements. Therefore, the goal of this study is to review the state‐of‐the‐art amplitude‐aided MTT methods and compare each other extensively. For a fair comparison, a unified MTT framework is developed, and various methods are implemented and compared based on the same framework. On the challenging visual MTT datasets, the implemented methods are evaluated for several aspects such as accuracy, speed, sensitivity, and stability. Moreover, this study presents a summary of the extensive evaluation and guideline to select an appropriate method for amplitude‐aided tracking.
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