Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105527
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Meng, X | - |
dc.creator | Li, Z | - |
dc.creator | Wang, S | - |
dc.creator | Karambakhsh, A | - |
dc.creator | Sheng, B | - |
dc.creator | Yang, P | - |
dc.creator | Li, P | - |
dc.creator | Mao, L | - |
dc.date.accessioned | 2024-04-15T07:34:51Z | - |
dc.date.available | 2024-04-15T07:34:51Z | - |
dc.identifier.issn | 0045-7906 | - |
dc.identifier.uri | http://hdl.handle.net/10397/105527 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Ltd | en_US |
dc.rights | ©2020 Elsevier Ltd. All rights reserved. | en_US |
dc.rights | ©2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
dc.rights | The following publication Meng, X., Li, Z., Wang, S., Karambakhsh, A., Sheng, B., Yang, P., ... & Mao, L. (2020). A video information driven football recommendation system. Computers & Electrical Engineering, 85, 106699 is available at https://doi.org/10.1016/j.compeleceng.2020.106699. | en_US |
dc.subject | Collaborative filtering | en_US |
dc.subject | Detection | en_US |
dc.subject | Movements analysis | en_US |
dc.subject | Recommendation | en_US |
dc.subject | Tracking | en_US |
dc.title | A video information driven football recommendation system | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 85 | - |
dc.identifier.doi | 10.1016/j.compeleceng.2020.106699 | - |
dcterms.abstract | Designing a football recommendation system requires collecting physical, technical and tactical information from football games. However, traditional technical and tactical statistics of football still depend on manual numbering, which is a huge labor consumption. Though GPS (Global Positioning System) devices could be applied to collect football data, they are very expensive and are forbidden in many football games. To solve these problems, we utilize video tracking to capture physical and tactical information of football players and propose a football recommendation system through combining players’ tracking techniques with recommendation algorithms. In our proposed system, the YOLOv2 (You Only Look Once version 2) algorithm and improved KCF (Kernelized Correlation Filter) method are applied to obtain and analyze the location information of football players. The proposed system could automatically recognize and track players according to match videos instead of using wearable GPS devices. Compared with GPS, the experimental results have shown that the data obtained from our system are much closer to reality and have lower standard deviation. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Computers and electrical engineering, July 2020, v. 85, 106699 | - |
dcterms.isPartOf | Computers and electrical engineering | - |
dcterms.issued | 2020-07 | - |
dc.identifier.scopus | 2-s2.0-85085249450 | - |
dc.identifier.eissn | 1879-0755 | - |
dc.identifier.artn | 106699 | - |
dc.description.validate | 202402 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-0290 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Key Research and Development Program of China; National Natural Science Foundation of China; Macau Science and Technology Development Fund; Science and Technology Commission of Shanghai Municipality | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 21842994 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Li_Video_Information_Driven.pdf | Pre-Published version | 5.36 MB | Adobe PDF | View/Open |
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