Tracking Objects Based on Multiple Particle Filters for Multipart Combined Moving Directions Information
Joint Authors
Ha, Ngo Duong
Shimizu, Ikuko
Bao, Pham The
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-17
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Object tracking is an important procedure in the computer vision field as it estimates the position, size, and state of an object along the video’s timeline.
Although many algorithms were proposed with high accuracy, object tracking in diverse contexts is still a challenging problem.
The paper presents some methods to track the movement of two types of objects: arbitrary objects and humans.
Both problems estimate the state density function of an object using particle filters.
For the videos of a static or relatively static camera, we adjusted the state transition model by integrating the movement direction of the object.
Also, we propose that partitioning the object needs tracking.
To track the human, we partitioned the human into N parts and, then, tracked each part.
During tracking, if a part deviated from the object, it was corrected by centering rotation, and the part was, then, combined with other parts.
American Psychological Association (APA)
Ha, Ngo Duong& Shimizu, Ikuko& Bao, Pham The. 2020. Tracking Objects Based on Multiple Particle Filters for Multipart Combined Moving Directions Information. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1138882
Modern Language Association (MLA)
Ha, Ngo Duong…[et al.]. Tracking Objects Based on Multiple Particle Filters for Multipart Combined Moving Directions Information. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1138882
American Medical Association (AMA)
Ha, Ngo Duong& Shimizu, Ikuko& Bao, Pham The. Tracking Objects Based on Multiple Particle Filters for Multipart Combined Moving Directions Information. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1138882
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138882