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Observation Centric and Central Distance Recovery for Tracking of Sports Athletes.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Observation Centric and Central Distance Recovery for Tracking of Sports Athletes./
Author:	Huang, Hsiang-Wei.
Description:	1 online resource (24 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-10.
Contained By:	Masters Abstracts International84-10.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29994733click for full text (PQDT)
ISBN:	9798379409531

Observation Centric and Central Distance Recovery for Tracking of Sports Athletes.
Huang, Hsiang-Wei.

Observation Centric and Central Distance Recovery for Tracking of Sports Athletes. - 1 online resource (24 pages)

Source: Masters Abstracts International, Volume: 84-10.

Thesis (M.El.Eng.)--University of Washington, 2023.

Includes bibliographical references

Multi-Object Tracking on humans has improved rapidly with the development of object detection and re-identification algorithms. However, multi-actor tracking over humans with similar appearance and non-linear movement can still be very challenging even for the state-of-the-art tracking algorithm. Current motion-based tracking algorithms often use Kalman Filter to predict the motion of an object, however, its linear movement assumption can cause failure in tracking when the target is not moving linearly. And for multi-player tracking over the sports field, because the players on the same team are usually wearing the same color of jersey, making re-identification even harder both in the short term and long term in the tracking process. In this work, we proposed a motion-based tracking algorithm and three post-processing pipelines for three sports including basketball, football, and volleyball, we successfully handle the tracking of the non-linear movement of players on the sports fields. Experimental results achieved a HOTA of 73.968 on the testing set of ECCV DeeperAction Challenge SportsMOT Dataset, showing the effectiveness of the proposed framework and its robustness in different sports including basketball, football, and volleyball.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379409531Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Central distance recoveryIndex Terms--Genre/Form:

542853
Electronic books.

Observation Centric and Central Distance Recovery for Tracking of Sports Athletes.
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500 $a Source: Masters Abstracts International, Volume: 84-10.
500 $a Advisor: Hwang, Jenq-Neng.
502 $a Thesis (M.El.Eng.)--University of Washington, 2023.
504 $a Includes bibliographical references
520 $a Multi-Object Tracking on humans has improved rapidly with the development of object detection and re-identification algorithms. However, multi-actor tracking over humans with similar appearance and non-linear movement can still be very challenging even for the state-of-the-art tracking algorithm. Current motion-based tracking algorithms often use Kalman Filter to predict the motion of an object, however, its linear movement assumption can cause failure in tracking when the target is not moving linearly. And for multi-player tracking over the sports field, because the players on the same team are usually wearing the same color of jersey, making re-identification even harder both in the short term and long term in the tracking process. In this work, we proposed a motion-based tracking algorithm and three post-processing pipelines for three sports including basketball, football, and volleyball, we successfully handle the tracking of the non-linear movement of players on the sports fields. Experimental results achieved a HOTA of 73.968 on the testing set of ECCV DeeperAction Challenge SportsMOT Dataset, showing the effectiveness of the proposed framework and its robustness in different sports including basketball, football, and volleyball.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 649834
650 4 $a Computer science. $3 523869
650 4 $a Sports management. $3 3423935
653 $a Central distance recovery
653 $a Sports athletes
653 $a Multi-object tracking
653 $a Kalman filter
653 $a Motion-based tracking algorithm
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710 2 $a University of Washington. $b Electrical Engineering. $3 2102369
773 0 $t Masters Abstracts International $g 84-10.
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