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Live sports event detection based on broadcast video and web-casting text

Published: 23 October 2006 Publication History

Abstract

Event detection is essential for sports video summarization, indexing and retrieval and extensive research efforts have been devoted to this area. However, the previous approaches are heavily relying on video content itself and require the whole video content for event detection. Due to the semantic gap between low-level features and high-level events, it is difficult to come up with a generic framework to achieve a high accuracy of event detection. In addition, the dynamic structures from different sports domains further complicate the analysis and impede the implementation of live event detection systems. In this paper, we present a novel approach for event detection from the live sports game using web-casting text and broadcast video. Web-casting text is a text broadcast source for sports game and can be live captured from the web. Incorporating web-casting text into sports video analysis significantly improves the event detection accuracy. Compared with previous approaches, the proposed approach is able to: (1) detect live event only based on the partial content captured from the web and TV; (2) extract detailed event semantics and detect exact event boundary, which are very difficult or impossible to be handled by previous approaches; and (3) create personalized summary related to certain event, player or team according to user's preference. We present the framework of our approach and details of text analysis, video analysis and text/video alignment. We conducted experiments on both live games and recorded games. The results are encouraging and comparable to the manually detected events. We also give scenarios to illustrate how to apply the proposed solution to professional and consumer services.

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Published In

cover image ACM Conferences
MM '06: Proceedings of the 14th ACM international conference on Multimedia
October 2006
1072 pages
ISBN:1595934472
DOI:10.1145/1180639
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 23 October 2006

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Author Tags

  1. broadcast video
  2. event detection
  3. web-casting text

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MM06
MM06: The 14th ACM International Conference on Multimedia 2006
October 23 - 27, 2006
CA, Santa Barbara, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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  • (2022)Real-time classification of handball game situations2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI56018.2022.00106(686-691)Online publication date: Oct-2022
  • (2022)Learning Pixel-Level Distinctions for Video Highlight Detection2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52688.2022.00308(3063-3072)Online publication date: Jun-2022
  • (2021)Video Text DetectionCognitively Inspired Video Text Processing10.1007/978-981-16-7069-5_4(61-94)Online publication date: 17-Nov-2021
  • (2021)Text and Non-text Frame Classification in VideoCognitively Inspired Video Text Processing10.1007/978-981-16-7069-5_3(35-60)Online publication date: 17-Nov-2021
  • (2020)Reading Both Single and Multiple Digital Video Clocks Using Context-Aware Pixel Periodicity and Deep LearningInternational Journal of Digital Crime and Forensics10.4018/IJDCF.202004010212:2(21-39)Online publication date: 1-Apr-2020
  • (2020)Techniques and applications for soccer video analysis: A surveyMultimedia Tools and Applications10.1007/s11042-020-09409-0Online publication date: 12-Aug-2020
  • (2020)Reading Digital Video Clocks by Two Phases of Connected Deep NetworksImage and Video Technology10.1007/978-3-030-39770-8_16(194-205)Online publication date: 27-Jan-2020
  • (2019)A Deep Learning Model for Extracting Live Streaming Video Highlights using Audience MessagesProceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference10.1145/3375959.3375965(75-81)Online publication date: 21-Dec-2019
  • (2019)Sports Video Captioning via Attentive Motion Representation and Group Relationship ModelingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2019.2921655(1-1)Online publication date: 2019
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