Discovering Homogeneous Groups from Geo-Tagged Videos
The popularity of intelligent devices with GPS and digital compasses has generated plentiful videos and images with text tags, timestamps, and geo-references. These digital footprints of travelers record their time and spatial movements and have become indispensable information resources, vital in a...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/9/4443 |
| _version_ | 1797601708145115136 |
|---|---|
| author | Xuejing Di Dong June Lew Kwang Woo Nam |
| author_facet | Xuejing Di Dong June Lew Kwang Woo Nam |
| author_sort | Xuejing Di |
| collection | DOAJ |
| description | The popularity of intelligent devices with GPS and digital compasses has generated plentiful videos and images with text tags, timestamps, and geo-references. These digital footprints of travelers record their time and spatial movements and have become indispensable information resources, vital in applications such as how groups of videographers behave and in future-movement prediction. In this paper, first we propose algorithms to discover homogeneous groups from geo-tagged videos with view directions. Second, we extend the density clustering algorithm to support fields-of-view (FoVs) in the geo-tagged videos and propose an optimization model based on a two-level grid-based index. We show the efficiency and effectiveness of the proposed homogeneous-pattern-discovery approach through experimental evaluation on real and synthetic datasets. |
| first_indexed | 2024-03-11T04:07:30Z |
| format | Article |
| id | doaj.art-fa2c18f86e574a34bb441470b5bcc675 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:07:30Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fa2c18f86e574a34bb441470b5bcc6752023-11-17T23:44:27ZengMDPI AGSensors1424-82202023-05-01239444310.3390/s23094443Discovering Homogeneous Groups from Geo-Tagged VideosXuejing Di0Dong June Lew1Kwang Woo Nam2School of Computer Science and Engineering, Kunsan National University, 558 Daehak-ro, Gunsan 54150, Republic of KoreaSchool of Computer Science and Engineering, Kunsan National University, 558 Daehak-ro, Gunsan 54150, Republic of KoreaSchool of Computer Science and Engineering, Kunsan National University, 558 Daehak-ro, Gunsan 54150, Republic of KoreaThe popularity of intelligent devices with GPS and digital compasses has generated plentiful videos and images with text tags, timestamps, and geo-references. These digital footprints of travelers record their time and spatial movements and have become indispensable information resources, vital in applications such as how groups of videographers behave and in future-movement prediction. In this paper, first we propose algorithms to discover homogeneous groups from geo-tagged videos with view directions. Second, we extend the density clustering algorithm to support fields-of-view (FoVs) in the geo-tagged videos and propose an optimization model based on a two-level grid-based index. We show the efficiency and effectiveness of the proposed homogeneous-pattern-discovery approach through experimental evaluation on real and synthetic datasets.https://www.mdpi.com/1424-8220/23/9/4443geo-tagged videosspatio–temporal databasesclusteringtrajectory pattern mining |
| spellingShingle | Xuejing Di Dong June Lew Kwang Woo Nam Discovering Homogeneous Groups from Geo-Tagged Videos Sensors geo-tagged videos spatio–temporal databases clustering trajectory pattern mining |
| title | Discovering Homogeneous Groups from Geo-Tagged Videos |
| title_full | Discovering Homogeneous Groups from Geo-Tagged Videos |
| title_fullStr | Discovering Homogeneous Groups from Geo-Tagged Videos |
| title_full_unstemmed | Discovering Homogeneous Groups from Geo-Tagged Videos |
| title_short | Discovering Homogeneous Groups from Geo-Tagged Videos |
| title_sort | discovering homogeneous groups from geo tagged videos |
| topic | geo-tagged videos spatio–temporal databases clustering trajectory pattern mining |
| url | https://www.mdpi.com/1424-8220/23/9/4443 |
| work_keys_str_mv | AT xuejingdi discoveringhomogeneousgroupsfromgeotaggedvideos AT dongjunelew discoveringhomogeneousgroupsfromgeotaggedvideos AT kwangwoonam discoveringhomogeneousgroupsfromgeotaggedvideos |