Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2020

04.02.2020

Content-Aware Summarization of Broadcast Sports Videos: An Audio–Visual Feature Extraction Approach

verfasst von: Abdullah Aman Khan, Jie Shao, Waqar Ali, Saifullah Tumrani

Erschienen in: Neural Processing Letters | Ausgabe 3/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

A large number of videos available on the internet belong to the category of sports. Generally, a sports video has a long duration and consists of only a few exciting moments. Sports enthusiasts keep themselves updated on the current happenings, in less time, by means of a summarized version of the sports video known as highlights. For the past few years, sports video summarization is regaining attention among the research community. Automatic generation of highlights form a sports video is a challenging task as different sports games have different rules and situations. In this paper, we propose a method for automatically generating highlights from broadcast sports videos. The proposed method generates highlights by extracting audio and visual features from a sports video. Our method automatically learns the scorebox template from a broadcast sports video using SIFT features, and then locates and extracts the template from a video stream. The extracted template is further analyzed to find out all the possible text regions. Afterward, the information is extracted from all the text regions by means of deep neural network. Based on user preferences, the most relevant information is extracted and converted to a keyframe representation which helps to generate highlights. Extensive experiments were performed to evaluate the effectiveness of the proposed method. Results of the experiments reveal the effectiveness and superiority of the proposed method.
Vorheriger Artikel Parameter-Free Extreme Learning Machine for Imbalanced Classification
Nächster Artikel Bipartite Synchronization Analysis of Fractional Order Coupled Neural Networks with Hybrid Control

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
Literatur
1.
Agyeman R, Muhammad R, Choi GS (2019) Soccer video summarization using deep learning. In: 2nd IEEE conference on multimedia information processing and retrieval, MIPR 2019, San Jose, CA, USA, March 28–30, 2019, pp 270–273
2.
Akiyama Y, Barrantes RG, Hynes T (2019) Video scene extraction tool for soccer goalkeeper performance data analysis. In: Joint proceedings of the ACM IUI 2019 workshops co-located with the 24th ACM conference on intelligent user interfaces (ACM IUI 2019), Los Angeles, USA, March 20, 2019
3.
Berkun R, Sonn E, Rudoy D (2011) Detection of score changes in sport videos using textual overlays. In: 7th international symposium on image and signal processing and analysis (ISPA), pp 301–306
4.
Bettadapura V, Pantofaru C, Essa IA (2016) Leveraging contextual cues for generating basketball highlights. In: Proceedings of the 2016 ACM conference on multimedia conference, MM 2016, Amsterdam, The Netherlands, October 15–19, 2016, pp 908–917
5.
Cai J, Tang X (2018) RGB video based tennis action recognition using a deep weighted long short-term memory. CoRR. abs/1808.00845
6.
Chakraborty S, Tickoo O, Iyer R (2015) Adaptive keyframe selection for video summarization. In: 2015 IEEE winter conference on applications of computer vision, WACV 2015, Waikoloa, HI, USA, January 5–9, 2015, pp 702–709
7.
Chen C, Chen L (2014) Novel framework for sports video analysis: a basketball case study. In: 2014 IEEE international conference on image processing, ICIP 2014, Paris, France, October 27–30, 2014, pp 961–965
8.
Chen D, Hsiao M, Lee S (2006) Automatic closed caption detection and filtering in MPEG videos for video structuring. J Inf Sci Eng 22(5):1145–1162
9.
Chen H, Tsai SS, Schroth G, Chen DM, Grzeszczuk R, Girod B (2011) Robust text detection in natural images with edge-enhanced maximally stable extremal regions. In: 18th IEEE international conference on image processing, ICIP 2011, Brussels, Belgium, September 11–14, 2011, pp 2609–2612
10.
Decroos T, Dzyuba V, Haaren JV, Davis J (2017) Predicting soccer highlights from spatio-temporal match event streams. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, February 4–9, 2017, San Francisco, California, USA, pp 1302–1308
11.
Deng G, Liu L, Zuo J (2019) Scoring framework of soccer matches using possession trajectory data. In: Proceedings of the ACM turing celebration conference—China, ACM TUR-C 2019, Chengdu, China, May 17–19, 2019, pp 59:1–59:2
12.
Ghosh A, Jawahar CV (2017) Smarttennistv: automatic indexing of tennis videos. In: Computer vision, pattern recognition, image processing, and graphics—6th national conference, NCVPRIPG 2017, Mandi, India, December 16–19, 2017, revised selected papers, pp 24–33
13.
Ghosh A, Jawahar CV (2018) SmartTennisTV: automatic indexing of tennis videos. CoRR. abs/1801.01430
14.
Gilbert AC, Zhang Y, Lee K, Zhang Y, Lee H (2017) Towards understanding the invertibility of convolutional neural networks. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, IJCAI 2017, Melbourne, Australia, August 19–25, 2017, pp 1703–1710
15.
Godi M, Rota P, Setti F (2017) Indirect match highlights detection with deep convolutional neural networks. In: New trends in image analysis and processing—ICIAP 2017—ICIAP international workshops, WBICV, SSPandBE, 3AS, RGBD, NIVAR, IWBAAS, and MADiMa 2017, Catania, Italy, September 11–15, 2017, revised selected papers, pp 87–96
16.
Gong Y, Sin LT, Chuan CH, Zhang H, Sakauchi M (1995) Automatic parsing of TV soccer programs. In: Proceedings of the IEEE international conference on multimedia computing and systems, ICMCS 1995, Washington DC, USA, May 15–18, 1995, pp 167–174
17.
Gonzalez Á, Bergasa LM, Torres JJY, Bronte S (2012) Text location in complex images. In: Proceedings of the 21st international conference on pattern recognition, ICPR 2012, Tsukuba, Japan, November 11–15, 2012, pp 617–620
18.
Guo J, Gurrin C, Lao S, Foley C, Smeaton AF (2011) Localization and recognition of the scoreboard in sports video based on SIFT point matching. In: Advances in multimedia modeling—17th international multimedia modeling conference, MMM 2011, Taipei, Taiwan, January 5–7, 2011, proceedings, Part II, pp 337–347
19.
20.
21.
Huang C, Shih H, Chao C (2006) Semantic analysis of soccer video using dynamic bayesian network. IEEE Trans Multimed 8(4):749–760CrossRef
22.
Hung M, Hsieh C (2008) Event detection of broadcast baseball videos. IEEE Trans Circuits Syst Video Technol 18(12):1713–1726CrossRef
23.
Javed A, Irtaza A, Malik H, Mahmood MT, Adnan SM (2019) Multimodal framework based on audio–visual features for summarisation of cricket videos. IET Image Process 13(4):615–622CrossRef
24.
Jiang H, Lu Y, Xue J (2016) Automatic soccer video event detection based on a deep neural network combined CNN and RNN. In: 28th IEEE international conference on tools with artificial intelligence, ICTAI 2016, San Jose, CA, USA, November 6–8, 2016, pp 490–494
25.
Kim W, Park J, Kim C (2008) Scorebox extraction from mobile sports videos using support vector machines. In: Proceedings of the SPIE 7073, applications of digital image processing XXXI
26.
Kim Y, Kim M (2019) ‘A wisdom of crowds’: social media mining for soccer match analysis. IEEE Access 7:52634–52639CrossRef
27.
Kosmadakis I, Petrellis N, Birbas MK, Vardakas M (2018) Employing Savitzky–Golay smoothing in a low cost ehealth platform. In: 41st international conference on telecommunications and signal processing, TSP 2018, Athens, Greece, July 4–6, 2018, pp 1–5
28.
Li Y, Lu H (2012) Scene text detection via stroke width. In: Proceedings of the 21st international conference on pattern recognition, ICPR 2012, Tsukuba, Japan, November 11–15, 2012, pp 681–684
29.
Liang C, Chu W, Kuo J, Wu J, Cheng W (2005) Baseball event detection using game-specific feature sets and rules. In: International symposium on circuits and systems (ISCAS 2005), 23–26 May 2005. Kobe, Japan, pp 3829–3832
30.
Liao S, Wang Y, Xin Y (2015) Research on scoreboard detection and localization in basketball video. Int J Multimed Ubiquitous Eng 10(11):57–68CrossRef
31.
Merler M, Joshi D, Nguyen Q, Hammer S, Kent J, Smith JR, Feris RS (2017) Automatic curation of golf highlights using multimodal excitement features. In: 2017 IEEE conference on computer vision and pattern recognition workshops, CVPR workshops 2017, Honolulu, HI, USA, July 21–26, 2017, pp 57–65
32.
Miao G, Zhu G, Jiang S, Huang Q, Xu C, Gao W (2007) The demo: a real-time score detection and recognition approach in broadcast basketball sports video. In: Proceedings of the 2007 IEEE international conference on multimedia and expo, ICME 2007, July 2–5, 2007, Beijing, China, p 1
33.
Mochizuki T, Tadenuma M, Yagi N (2005) Baseball video indexing using patternization of scenes and hidden Markov model. In: Proceedings of the 2005 international conference on image processing, ICIP 2005, Genoa, Italy, September 11–14, 2005, pp 1212–1215
34.
Narasimhan H, Satheesh S, Sriram D (2010) Automatic summarization of cricket video events using genetic algorithm. In: Genetic and evolutionary computation conference, GECCO 2010, proceedings, Portland, Oregon, USA, July 7–11, 2010, companion material, pp 2051–2054
35.
Neumann L, Matas J (2012) Real-time scene text localization and recognition. In: 2012 IEEE conference on computer vision and pattern recognition, Providence, RI, USA, June 16–21, 2012, pp 3538–3545
36.
Raventos A, Quijada R, Torres L, Tarres F, Carasusán E, Giribet D (2014) The importance of audio descriptors in automatic soccer highlights generation. In: IEEE 11th international multi-conference on systems, signals and devices, SSD 2014, Castelldefels-Barcelona, Spain, February 11–14, 2014, pp 1–6
37.
Rekik G, Khacharem A, Belkhir Y, Bali N, Jarraya M (2019) The instructional benefits of dynamic visualizations in the acquisition of basketball tactical actions. J Comput Assist Learn 35(1):74–81CrossRef
38.
Roy S, Shivakumara P, Pal U, Lu T, Tan CL (2016) New tampered features for scene and caption text classification in video frame. In: 15th international conference on frontiers in handwriting recognition, ICFHR 2016, Shenzhen, China, October 23–26, 2016, pp 36–41
39.
Sankar KP, Pandey S, Jawahar CV (2006) Text driven temporal segmentation of cricket videos. In: Computer vision, graphics and image processing, 5th Indian conference, ICVGIP 2006, Madurai, India, December 13–16, 2006, proceedings, pp 433–444
40.
Santiago CB, Sousa A, Estriga ML, Reis LP, Lames M (2010) Survey on team tracking techniques applied to sports. In: Autonomous and intelligent systems—first international conference, AIS 2010, Povoa de Varzim, Portugal, June 21–23, 2010. Proceedings, pp 1–6
41.
Setti F, Conigliaro D, Rota P, Bassetti C, Conci N, Sebe N, Cristani M (2017) The s-hock dataset: a new benchmark for spectator crowd analysis. Comput Vis Image Underst 159:47–58CrossRef
42.
Shih H (2018) A survey of content-aware video analysis for sports. IEEE Trans Circuits Syst Video Technol 28(5):1212–1231CrossRef
43.
Shih H, Huang C (2006) A robust superimposed caption box content understanding for sports videos.In: Eigth IEEE international symposium on multimedia (ISM 2006), 11–13 December 2006, San Diego, CA, USA, pp 867–872
44.
Shukla P, Sadana H, Bansal A, Verma D, Elmadjian CEL, Raman B, Turk M (2018) Automatic cricket highlight generation using event-driven and excitement-based features. In: 2018 IEEE conference on computer vision and pattern recognition workshops, CVPR workshops 2018, Salt Lake City, UT, USA, June 18–22, 2018, pp 1800–1808
45.
Smith R, Antonova D, Lee D (2009) Adapting the Tesseract open source OCR engine for multilingual OCR. In: Proceedings of the international workshop on multilingual OCR, MOCR@ICDAR 2009, Barcelona, Spain, July 25, 2009, p 1
46.
Tang H, Kwatra V, Sargin ME, Gargi U (2011) Detecting highlights in sports videos: cricket as a test case. In: Proceedings of the 2011 IEEE international conference on multimedia and expo, ICME 2011, 11–15 July, 2011, Barcelona, Catalonia, Spain, pp 1–6
47.
Tran D, Bourdev LD, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3d convolutional networks. In: 2015 IEEE international conference on computer vision, ICCV 2015, Santiago, Chile, December 7–13, 2015, pp 4489–4497
48.
Uehira K, Tanaka G, Suzuki K, Komiya K, Ikeda H (2012) Content indexing for specific scenes in baseball videos utilizing two-dimensional matching of intensity patterns. In: IEEE international conference on consumer electronics, ICCE 2012, Las Vegas, NV, USA, January 13–16, 2012, pp 122–123
49.
Yoon Y, Hwang H, Choi Y, Joo M, Oh H, Park I, Lee K, Hwang J (2019) Analyzing basketball movements and pass relationships using realtime object tracking techniques based on deep learning. IEEE Access 7:56564–56576CrossRef
50.
Yu J, Lei A, Hu Y (2019) Soccer video event detection based on deep learning. In: MultiMedia modeling—25th international conference, MMM 2019, Thessaloniki, Greece, January 8–11, 2019, proceedings, Part II, pp 377–389
51.
Zhang D, Chang S (2002) Event detection in baseball video using superimposed caption recognition. In: Proceedings of the 10th ACM international conference on multimedia 2002, Juan les Pins, France, December 1–6, 2002, pp 315–318
52.
Zhang F, Jiang Y (2019) Basketball action data processing method based on mode symmetric algorithm. Symmetry 11(4):560CrossRef
53.
54.
Zhu X, Zhang S, He W, Hu R, Lei C, Zhu P (2019) One-step multi-view spectral clustering. IEEE Trans Knowl Data Eng 31(10):2022–2034CrossRef
Metadaten
Titel
Content-Aware Summarization of Broadcast Sports Videos: An Audio–Visual Feature Extraction Approach
verfasst von
Abdullah Aman Khan
Jie Shao
Waqar Ali
Saifullah Tumrani
Publikationsdatum
04.02.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10200-3

Weitere Artikel der Ausgabe 3/2020

Neural Processing Letters 3/2020 Zur Ausgabe

OriginalPaper

Adaptive Synchronization of Complex Dynamical Networks via Distributed Pinning Impulsive Control

OriginalPaper

Using Locality Preserving Projections to Improve the Performance of Kernel Clustering

OriginalPaper

Stochastic One-Step Training for Feedforward Artificial Neural Networks

OriginalPaper

Parameter-Free Extreme Learning Machine for Imbalanced Classification

OriginalPaper

Low-Rank Discriminative Adaptive Graph Preserving Subspace Learning

OriginalPaper

Convergence of Batch Gradient Method Based on the Entropy Error Function for Feedforward Neural Networks

Neuer Inhalt

    Bildnachweise