Advertisement

Multimedia Tools and Applications

, Volume 55, Issue 3, pp 443–481 | Cite as

A comprehensive study of visual event computing

  • WeiQi Yan
  • Declan F. Kieran
  • Setareh Rafatirad
  • Ramesh Jain
Article

Abstract

This paper contains a survey on aspects of visual event computing. We start by presenting events and their classifications, and continue with discussing the problem of capturing events in terms of photographs, videos, etc, as well as the methodologies for event storing and retrieving. Later, we review an extensive set of papers taken from well-known conferences and journals in multiple disciplines. We analyze events, and summarize the procedure of visual event actions. We introduce each component of a visual event computing system, and its computational aspects, we discuss the progress of each component and review its overall status. Finally, we suggest future research trends in event computing and hope to introduce a comprehensive profile of visual event computing to readers.

Keywords

Visual events Search Retrieval Mining Reasoning 

Notes

Acknowledgements

We appreciate for the great help from the colleagues of Queen’s University Belfast(QUB): Prof. Danny Crookes, Dr. Weiru Liu, Dr. Paul Miller, and Dr. Xiwu Gu etc. This work was partially supported by QUB research project: Unusual event detection in audio-visual surveillance for public transport (NO.D6223EEC).

References

  1. 1.
    Adams B, Venkatesh S (2005) Situated event bootstrapping and capture guidance for automated home movie authoring. In: Proc. of ACM Multimedia’05, Singapore, pp 754–763Google Scholar
  2. 2.
    Alahari K, Jawahar C (2006) Discriminative actions for recognising events. In: Proc. of ICVGIP’06. LNCS, vol 4338, India, pp 552–1563Google Scholar
  3. 3.
    Al-Hames M, Rigoll G (2005) A multi-modal mixed-state dynamic bayesian network for robust meeting event recognition from disturbed data. In: Proc. of IEEE ICME’05, Amsterdam, The Netherlands, pp 45–48Google Scholar
  4. 4.
    Alahari K, Jawahar C (2006) Dynamic events as mixtures of spatial and temporal features. In: Proc. of ICVGIP’06. LNCS vol 4338, India, pp 540–551Google Scholar
  5. 5.
    Alan Fern RG, Siskind JM (2002) Learning temporal, relational, force-dynamic event definitions from video. In: Proc. of AAAI’02, Palo Alto, California, pp 159–166Google Scholar
  6. 6.
    Amer A, Dubois E, Mitiche A (2002) Context-independent real-time event recognition: application to key-image extraction. In: Proc. of IEEE ICPR’02, Quebec, Canada, pp 945–948Google Scholar
  7. 7.
    Amera A, Duboisb E, Mitichec A (2005) Rule-based real-time detection of context-independent events in video shots. Real-Time Imaging 11(3):244–256CrossRefGoogle Scholar
  8. 8.
    Andrade EL, Blunsden S, Fisher RB (2006) Modeling crowd scenes for event detection. In: Proc. of ICPR’06, Hong Kong, China, pp 175–178Google Scholar
  9. 9.
    Appan P, Sundaram H (2004) Networked multimedia event exploration. In: Proc. of ACM multimedia. New York City, USA, pp 40–47Google Scholar
  10. 10.
    Atrey PK, Kankanhalli MS, Jain R (2006) Information assimilation framework for event detection in multimedia surveillance systems. Springer/ACM Multimedia Syst J 12(3):239–253CrossRefGoogle Scholar
  11. 11.
    Babaguchi N, Kawai Y, Kitahashi T (2002) Event based indexing of broadcasted sports video by intermodal collaboration. IEEE Trans Multimedia 12(3)68–75CrossRefGoogle Scholar
  12. 12.
    Babaguchi N, Sasamori S, Kitahashi T, Jain R (1999) Detecting events from continuous media by intermodal collaboration and knowledge use. In: Proc. of IEEE ICMCS’99, Firenze, Italy, pp 782–786Google Scholar
  13. 13.
    Barnard M, Odobez J-M (2005) Sports event recognition using layered hmms. In: Proc. of IEEE ICME’05, Amsterdam, The Netherlands, pp 1150–1153Google Scholar
  14. 14.
    Baulier J, Blott S, Korth HF, Silberschatz A (1998) A database system for real-time event aggregation in telecommunication. In: Proc. of VLDB’98, pp 680–684, New York, USAGoogle Scholar
  15. 15.
    Behera A, Lalanne D, Ingold R (2004) Looking at projected documents: event detection & document identification. In: Proc. of IEEE ICME’04, Taipei, pp 2127–2130Google Scholar
  16. 16.
    Bertini M, Bimbo AD, Cucchiara R, Prati A (2004) Object-based and event-based semantic video adaptation. In: Proc. of IEEE ICPR’04, Cambridge, UK, pp 987–990Google Scholar
  17. 17.
    Black MJ (1999) Explaining optical flow events with parameterized spatio-temporal models. In: Proc. of IEEE CVPR’99, Ft Collins, USA, pp 326–332Google Scholar
  18. 18.
    Bonzanini A, Leonardi R, Migliorati P (2001) Event recognition in sport programs using low-level motion indices. In: Proc. of IEEE ICME’01, Tokyo, Japan, pp 2127–2130Google Scholar
  19. 19.
    Boykin S, Merlino A (2000) Machine learning of event segmentation for news on demand. Commun ACM 43(2):35–41CrossRefGoogle Scholar
  20. 20.
    Burges CJ (1998) A tutorial on Support Vector Machines for pattern recognition. Data Min Knowl Disc 2:121–167CrossRefGoogle Scholar
  21. 21.
    Chan MT, Hoogs A, Schmiederer J, Petersen M (2004) Detecting rare events in video using semantic primitives with HMM. In: Proc. of IEEE ICPR’04, Cambridge, UK, pp 150–154Google Scholar
  22. 22.
    Chan MT, Hoogs A, Sun Z, Schmiederer J, Bhotika R, Doretto G (2006) Event recognition with fragmented object tracks. In: Proc. of IEEE ICPR’06, HongKong, China, pp 412–416Google Scholar
  23. 23.
    Chan MT, Hoogs A, Bhotika R, Perera AGA, Schmiederer J, Doretto G (2006) Joint recognition of complex events and track matching. In: Proc. of IEEE CVPR’06, New York, USA, pp 1615–1622Google Scholar
  24. 24.
    Chu W-T, Wu J-L (2005) Integration of rule-based and model-based decision methods for baseball event detection. In: Proc. of IEEE ICME’05, Amsterdam, The Netherlands, pp 137–140Google Scholar
  25. 25.
    Cooper M, Foote J, Girgensohn A, Wilcox L (2005) Temporal event clustering for digital photo collections. ACM Trans on TOMCCAP 1(3):269–288Google Scholar
  26. 26.
    Cui P, Sun L, Liu Z-Q, Yang S (2007) A sequential monte carlo approach to anomaly detection in tracking visual events. In: Proc of IEEE CVPR’07, Minnesota, USAGoogle Scholar
  27. 27.
    Dai S, Dhawan AP (2007) Adaptive learning for event modeling and characterization. Pattern Recogn 40(5):1544–1555MATHCrossRefGoogle Scholar
  28. 28.
    Demers A, Gehrke J, Hong M, Riedewald M, White W (2005) A general algebra and implementation for monitoring event streams. Cornell University, Tech Rep TR2005-1997Google Scholar
  29. 29.
    Engle JC, Odutola A (2006) Control field event detection in a digital video recorder. US Patent 5699124Google Scholar
  30. 30.
    Fern A, Givan R, Siskind JM (2002) Specific-to-general learning for temporal events. In: Proc. of AAAI’02, Palo Alto, USA, pp 152–158Google Scholar
  31. 31.
    Foresti GL, Marcenaro L, Regazzoni CS (2002) Automatic detection and indexing of video event shots for surveillance applications. IEEE Trans Multimedia 4(4):459–471CrossRefGoogle Scholar
  32. 32.
    Foresti GL, Micheloni C, Snidaro L (2004) Event classification for automatic visual-based surveillance of parking lots. In: Proc. of IEEE ICPR’04, Cambridge, UK, pp 314–317Google Scholar
  33. 33.
    Franois ARJ, Nevatia R, Hobbs JR, Bolles RC (2003) VERL: an ontology framework for representing and annotating video events. IEEE Multimed 76:269–288Google Scholar
  34. 34.
    Frawley GP-S W, Matheus C (1992) Knowledge discovery in databases: an overview. AI Mag 13(3):213–228Google Scholar
  35. 35.
    Gehani NH, Jagadish HV, Shmueli O (1992) Composite event specification in active databases: model & implementation. In: Proc. of VLDB’92, Vancouver, Canada, pp 327–338Google Scholar
  36. 36.
    Ghahramani Z (1998) Adaptive processing of sequences and data structures, lecture notes in artificial intelligence. ch. Learning Dynamic Bayesian Networks. Springer-Verlag, Berlin, pp 168–197Google Scholar
  37. 37.
    Ghanem N, DeMenthon D, david Doermann, Davis L (2004) Representation and recognition of events in surveillance video using Petri nets. In: Proc. of workshop on event mining, Madison, USA, vol 7, no 7, p 112Google Scholar
  38. 38.
    Gu H, Ji Q (2004) Facial event classification with task oriented dynamic Bayesian network. In: Proc. of IEEE CVPR’04, Reno, USA, pp 870–875Google Scholar
  39. 39.
    Haering NC, Qian RJ, Sezan MI (2000) A semantic event-detection approach and its application to detecting hunts in wildlife video. IEEE Trans Circuits Syst Video Technol 6(10):857–868CrossRefGoogle Scholar
  40. 40.
    Hakeem A, Shah M (2005) Multiple agent event detection and representation in videos. In: Proc. of AAAI’05, Pittsburgh, USA, pp 89–94Google Scholar
  41. 41.
    Hakeem A, Sheikh Y, Shah M (2004) Casee: a hierarchical event representation for the analysis of videos. In: Proc. of AAAI’04. San Jose, USA, pp 263–268Google Scholar
  42. 42.
    Hamid R, Johnson AY, Batta S, Bobick AF, Isbell CL, Coleman G (2005) Detection and explanation of anomalous activities: representing activities as bags of event n-grams. In: Proc. of IEEE CVPR’05. San Diego, USA, pp 1031–1038Google Scholar
  43. 43.
    Hand HMD, Smyth P (2001) Principles of data mining. MIT Press, Cambridge, USAGoogle Scholar
  44. 44.
    Haynes S, Jain R (1984) Low level motion events, trajectory discontinuities. In: Proc. of the first conference on artificial intelligence applications. San Diego, USA, pp 251–256Google Scholar
  45. 45.
    Haynes S, Jain R (1984) Event detection and correspondence. In: Proc. of Optical engineering, San Diego, USA, pp 251–256Google Scholar
  46. 46.
    Hongeng S (2004) Unsupervised learning of multi-object event classes. In: Proc. of the 15th British machine vision conference (BMVC’04). London, UKGoogle Scholar
  47. 47.
    Hongeng S, Nevatia R (2003) Large-scale event detection using Semi-Hidden Markov Models. In: Proc. of IEEE ICCV’03. Nice, France, pp 1455–1462Google Scholar
  48. 48.
    Hopkins M (2002) Strategies for determining causes of events. In: Proc. of AAAI’02. Palo Alto, California, pp 546–552Google Scholar
  49. 49.
    Johnson N, Hogg DC (1995) Learning the distribution of object trajectories for event recognition. In: Proc. of the 6th British conference on machine vision, Surrey, UK, pp 583–592Google Scholar
  50. 50.
    Joo S-W, Chellappa R (2006) Attribute grammar-based event recognition and anomaly detection. In: Proc. of CVPRW’06, New York, USA, pp 107–115Google Scholar
  51. 51.
    Jung Y-K, Lee K-W, Ho Y-S (2001) Content-based event retrieval using semantic scene interpretation for automated traffic surveillance. IEEE Trans Intell Transp Syst 2(3):151–163CrossRefGoogle Scholar
  52. 52.
    Kang H-B (2002) Analysis of scene context related with emotional events. In: Proc. of ACM Multimedia’02, Juan Les Pins, France, pp 311–314Google Scholar
  53. 53.
    Kawashima H, Matsuyama T (2002) Integrated event recognition from multiple sources. In: Proc. of IEEE ICPR’02, Quebec, Canada, pp 785–789Google Scholar
  54. 54.
    Ke Y (2005) Efficient visual event detection using volumetric features. In: Proc. of IEEE ICCV’05, Beijing, China, pp 166–173Google Scholar
  55. 55.
    Ke Y, Sukthankar R, Hebert M (2007) Event detection in crowded videos. In: Proc of IEEE ICCV’07, Rio de Janeiro, BraziGoogle Scholar
  56. 56.
    Krzysztof W, Cios P, Swiniarski R (1998) Data mining methods for knowledge discovery. Kluwer, Norwell, MAMATHGoogle Scholar
  57. 57.
    Lee D, Yannakakis M (1996) Principles and methods of testing finite state machines—a survey. Proc IEEE 84(8):1090–1122CrossRefGoogle Scholar
  58. 58.
    Li L-J, Li F-F (2007) What, where and who? classifying events by scene and object recognition. In: Proc of IEEE ICCV’07, Rio de Janeiro, BraziGoogle Scholar
  59. 59.
    Li C-H, Chiu C-Y, Huang C-R, Chen C-S, Chien L-F (2006) Image content clustering and summarization for photo collection. In: Proc. of IEEE ICME’06, CanadaGoogle Scholar
  60. 60.
    Lie W-N, Shia S-H (2005) Combining caption and visual features for semantic event classification of baseball video. In: Proc. of IEEE ICME’05, Amsterdam, The Netherlands, pp 1254–1257Google Scholar
  61. 61.
    Lie W-N, Lin T-C, Hsia S-H (2004) Motion-based event detection and semantic classification for baseball sport videos. In: Proc. of IEEE ICME’04, Taipei, Taiwan, pp 1567–1570Google Scholar
  62. 62.
    Lim J-H, Tian Q, Mulhem P (2003) Home photo content modeling for personalized event-based retrieval. IEEE Multimed 10(4):28–37CrossRefGoogle Scholar
  63. 63.
    Loui AC, Savakis AE (2001) Automatic image event segmentation and quality screening for albuming applications. In: Proc. of IEEE ICME’01, Tokyo, Japan, pp 1125–1128Google Scholar
  64. 64.
    Loui AC, Savakis AE (2003) Automated event clustering and quality screening of consumer pictures for digital albuming. IEEE Trans Multimedia 10(4):390–402CrossRefGoogle Scholar
  65. 65.
    Lu C, Ferrier NJ (2004) Repetitive motion analysis: segmentation and event classification. IEEE Trans PAMI 26(2):258–263CrossRefGoogle Scholar
  66. 66.
    Ma Y, Bazakos M, Miller B, Buddharaju P (2006) Activity awareness: from predefined events to new pattern discovery. In: Proc. of ICVS’06, p 11Google Scholar
  67. 67.
    Malaia E (2006) Event structure representation in ontological semantics. In: Proc. of MLMTA (international conference on machine learning models, technologies & applications), Las Vegas, USA, pp 36–42Google Scholar
  68. 68.
    Matthew AG, Cooper D, Foote J, Wilcox L (2003) Temporal event clustering for digital photo collections. In: Proc. of ACM multimedia’03, Berkely, USA, pp 364–373Google Scholar
  69. 69.
    Mei T, Wang B, Hua X-S, Zhou H-Q, Li S (2006) Probabilistic multimodality fusion for event based home photo clustering. In: Proc. of IEEE ICME’06, Canada, pp 1757–1760Google Scholar
  70. 70.
    Miyauchi S, Hirano A, Babaguchi N, Kitahashi T (2002) Collaborative multimedia analysis for detecting semantical events from broadcasted sports video. In: Proc. of ICPR’02, Tokyo, Japan, pp 1009–1012Google Scholar
  71. 71.
    Mustafa A, Sethi I (2005) Detecting retail events using moving edges. In: Proc. of AVSS 2005, pp 626–631Google Scholar
  72. 72.
    Naaman M, Harada S, Wang Q (2004) Context data in geo-referenced digital photo collections. In: Proc. of ACM multimedia, New York, NY, USA, pp 196–203Google Scholar
  73. 73.
    Naaman M, Yeh RB, Garcia-Molina H, Paepcke A (2005) Leveraging context to resolve identity in photo albums. In: Proc. of the 5th ACM/IEEE-CS joint conference on digital libraries, Denver, CO, USA, pp 178–187Google Scholar
  74. 74.
    Naphade M, Huang T (2002) Discovering recurrent events in video using unsupervised methods. In: Proc. of IEEE ICIP’02Google Scholar
  75. 75.
    Naphade MR, Garg A, Huang TS (1997) Duration dependent input output markov models for audio-visual event detection. In: Proc. of IEEE ICME’01, Tokyo, Japan, pp 369–372Google Scholar
  76. 76.
    Nevatia R, Hobbs J, Bolls B (2004) An ontology for video event representation. In: Proc. of CVPRW’04, Washington, USA, vol 9, no 27, p 119Google Scholar
  77. 77.
    Nitta N, Babaguchi N, Kitahashi T (2000) Extracting actors, actions and events from sports video—a fundamental approach to story tracking. In: Proc of IEEE ICPR’00, Barcelona, Spain, pp 4718–4721Google Scholar
  78. 78.
    Nishida T, Kamijo S, Ikeuchi K (2001) Automated system of acquiring and visualizing track event statistics from track images. In: Proc. of IEEE ICME’01, Tokyo, Japan, pp 169–172Google Scholar
  79. 79.
    O’Hare N, Gurrin C, Lee H, Murphy N, Smeaton AF, Jones GJ (2005) Digital photos: where and when? In: Proc. of ACM multimedia’05, SingaporeGoogle Scholar
  80. 80.
    Okadome T (2006) Event representation for sensor data grounding. International Journal of Computer Science and Network Security 6(10):129–162Google Scholar
  81. 81.
    Osadchy M, Keren D (2004) A rejection-based method for event detection in video. IEEE Trans Circuits Syst Video Technol 4(14):534–541CrossRefGoogle Scholar
  82. 82.
    Pack D, Singh R, Brennan S, Jain R (2004) An event model and its implementation for multimedia information representation and retrieval. In: Proc. of IEEE ICME’04, Taipei, Taiwan, pp 1611–1614Google Scholar
  83. 83.
    Park S, Aggarwal JK (2004) Event semantics in two-person interactions. In: Proc. of IEEE ICPR’04, Taipei, Taiwan, pp 227–230Google Scholar
  84. 84.
    Peyrard N, Bouthemy P (2003) Detection of meaningful events in videos based on a supervised classification approach. In: Proc. of IEEE ICIP’03, pp 621–625Google Scholar
  85. 85.
    Piater JH, Richetto S, Crowley JL (2002) Event-based activity analysis in live video using a generic object tracker. In: Proc. of third IEEE international workshop on performance evaluation of tracking and surveillance, Copenhagen, pp 1–8Google Scholar
  86. 86.
    Pingali GS, Jean Y, Opalach A, Carlbom I (2001) Lucentvision: converting real world events into multimedia experiences. In: Proc. of IEEE ICME’01, Tokyo, Japan, pp 1433–1436Google Scholar
  87. 87.
    Pinzon J, Singh R, Taube W, Galan J (2006) Designing interactions in event-based unified management of personal multimedia information. In: Proc. of IEEE ICME’06, Canada, pp 337–340Google Scholar
  88. 88.
    Piriou G, Bouthemy P, Yao J-F (2004) Learned probabilistic image motion models for event detection in videos. In: Proc. of IEEE ICPR’04, Tokyo, Japan, pp 207–210Google Scholar
  89. 89.
    Qian RJ, Haering NC, Sezan MI (1999) A computational approach to semantic event detection. In: Proc. of IEEE CVPR’99, Ft Collins, USA, pp 200–206Google Scholar
  90. 90.
    Qiu G, Feng X, Fang J (2004) Compressing histogram representations for automatic color photo categorization. Pattern Recogn 37:2177–2193CrossRefGoogle Scholar
  91. 91.
    Quinton A (1979) Objects and events. Mind 88(350):197–214CrossRefGoogle Scholar
  92. 92.
    Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286CrossRefGoogle Scholar
  93. 93.
    Rao C, Shah M (2001) View-invariant representation and learning of human action. In: Proc. of IEEE workshop on detection and recognition of events in video, Vancouver, Canada, pp 55–63Google Scholar
  94. 94.
    Rao C, Shah M, Syeda-Mahmmod T (2003) Invariance in motion analysis of videos. In: Proc. of ACM multimedia’03, Bekerley, USA, pp 518–527Google Scholar
  95. 95.
    Reiter S, Rigoll G (2004) Segmentation and classification of meeting events using multiple classifier fusion and dynamic programming. In: Proc. of IEEE ICPR’04, Cambridge, UK, pp 434–437Google Scholar
  96. 96.
    Remagnino P, Jones G (2001) Classifying surveillance events from attributes and behaviour. In: Proc. of British machine vision conf, Manchester, UK, pp 685–694Google Scholar
  97. 97.
    Reiter S, Schuller B, Rigoll G (2006) Segmentation and recognition of meeting events using a two-layered hmm and a combined mlp-hmm approach. In: Proc. of IEEE ICME’06, Canada, pp 953–956Google Scholar
  98. 98.
    Saad MS, Khan M (2006) A multiview approach to tracking people in crowded scenes using a planar homography constraint. In: Proc. of ECCV’06, Graz, Austria, pp 133–146Google Scholar
  99. 99.
    Sadlier D, O’Connor NE (2005) Event detection based on generic characteristics of field-sports. In: Proc. of IEEE ICME’05, Amsterdam, The Netherlands, pp 759–762Google Scholar
  100. 100.
    Satoh Y, Tanahashi H, Wang C, Kaneko S, Niwa Y, Yamamoto K (2002) Robust event detection by radial reach filter (RRF). In: Proc. of IEEE ICPR’02, Quebec, Canada, pp 623–626Google Scholar
  101. 101.
    Schwalb E, Kask K, Dechter R (1994) Temporal reasoning with constraints on fluents and events. In: Proc. of AAAI’94, Seattle, USA, pp 1067–1072Google Scholar
  102. 102.
    Shotton DM, Rodríguez A, Guil N, Trelles O (2000) Object tracking and event recognition in biological microscopy videos. In: Proc. of IEEE ICPR’00, Seattle, USA, pp 4226–4229Google Scholar
  103. 103.
    Sinha SN, Pollefeys M (2005) Synchronization and calibration of a camera network for 3D event reconstruction from live video. In: Proc. of IEEE CVPR’05, San Diego, USA, p 1196Google Scholar
  104. 104.
    Siskind JM (2002) Visual event classification via force dynamics. In: Proc of AAAI’02, San Diego, USA, pp 149–155Google Scholar
  105. 105.
    Siskind JM, Morris Q (1996) A maximum-likelihood approach to visual event classification. In: Proc. of ECCV’96. LNCS, vol 1065, London, UK, pp 347–360Google Scholar
  106. 106.
    Smith PN, da Vitoria Lobo, Shah M (2002) Temporalboost for event recognition. In: Proc. of IEEE ICCV’05, San Diego, CA, USA, pp 733–740Google Scholar
  107. 107.
    Snoek C, Worring M (2006) Multimedia event-based video indexing using time intervals. Trans Multimedia 10(4):638–647Google Scholar
  108. 108.
    Syeda-Mahmood T (2002) Retrieving actions embedded in video. In: Proc. of ACM Multimedia’02, Juan Lins Pins, France, pp 513–522Google Scholar
  109. 109.
    Syeda-Mahmood T, Srinivasan S (2000) Detecting topical events in digital video. In: Proc. of ACM multimedia’00. Marina del Rey, Los Angeles, USA, pp 85–94CrossRefGoogle Scholar
  110. 110.
    Syeda-Mahmood T, Vasilescu A (2001) Recognizing action events from multiple view points. In: Proc. of IEEE workshop on detection and recognition of events in video 2001, Las Palmas, USA, pp 64–72Google Scholar
  111. 111.
    Tang Q, Koprinska I, Jin JS (2005) Content-adaptive transmission of reconstructed soccer goal events over low bandwidth networks. In: Proc. of ACM Multimedia’05, Singapore, pp 271–274Google Scholar
  112. 112.
    Teisseire M, Poncelet P, Cicchetti R (1994) Towards event-driven modelling for database design. In: Proc. of VLDB’94. Santiago de Chile, Chile, pp 285–296Google Scholar
  113. 113.
    Teraguchi M, Masumitsu K, Echigo T, Sekiguchi S, Etoh M (2002) Rapid generation of event-based indexes for personalized video digests. In: Proc of IEEE ICPR’02, Quebec, Canada, pp 1041–1044Google Scholar
  114. 114.
    Tesic J, Newsam S, Manjunath B (2002) Scalable spatial event representation. In: Proc. of IEEE ICME’02. Lausanne, Switzerland, pp 229–232Google Scholar
  115. 115.
    Thawani A, Gopalan S, Sridhar V (2004) Event driven semantics based ad selection. In: Proc. of IEEE ICME’04. Taipei, Taiwan, pp 1875–1878Google Scholar
  116. 116.
    Trausti TSH, Kristjansson T, Brendan Frey J (2001) Event-coupled hidden Markov models. In: Proc. of IEEE ICME’01, Tokyo, Japan, pp 385–388Google Scholar
  117. 117.
    Tong X-F, Lu H-Q, Liu Q-S (2004) A three-layer event detection framework and its application in soccer video. In: Proc. of IEEE ICME’04, Taipei, Taiwan, pp 1551–1554Google Scholar
  118. 118.
    Tovinkere V, Qian RJ (2001) Detecting semantic events in soccer games: towards a complete solution. In: Proc. of IEEE ICME’01, Tokyo, Japan, pp 1551–1554Google Scholar
  119. 119.
    Vassiliou A, Salway A, Pitt D (2004) Formalizing stories sequences of events and state changes. In: Proc. of IEEE ICME’04. Taipei, Taiwan, pp 587–590Google Scholar
  120. 120.
    Veeraraghavan H, Papanikolopoulos N, Schrater P (2007) Learning dynamic event descriptions in image sequences. In: Proc. of IEEE CVPR’07, Minnesota, USA, pp 1–6Google Scholar
  121. 121.
    Welch G, Bishop G (2001) An introduction to the Kalman filter. In: Proc. of ACM SIGGRPH’01, Los Angeles, USAGoogle Scholar
  122. 122.
    Westermann U, Jain R (2006) Toward a common event model for multimedia applications. International Journal on Semantic Web & Information Systems 14(1):19–29Google Scholar
  123. 123.
    Worboys MF, Hornsby K (2004) From objects to events: gem, the geospatial event model. In: Proc. of GIScience’04, Adelphi, USAGoogle Scholar
  124. 124.
    Xiang T, Gong S, Parkinson D (2002) Autonomous visual events detection and classification without explicit object-centred segmentation and tracking. In: Proc. of British machine vision conference, Cardiff, UK, pp 685–694Google Scholar
  125. 125.
    Xu H, Chua T-S (2004) The fusion of audio-visual features and external knowledge for event detection in team sports video. In: Proc. of ACM SIGMM international workshop on multimedia information retrieval, New York, USAGoogle Scholar
  126. 126.
    Xu H, Chua T-S (2006) Fusion of AV features and external information sources for event detection in team sports video. ACM TOMCCAP 2(1):44–67CrossRefGoogle Scholar
  127. 127.
    Xu H, Fong T-H, Chua T-S (2005) Fusion of multiple asynchronous information sources for event detection in soccer video. In: Proc. of IEEE ICME’05, Amsterdam, The Netherlands, pp 1242–1245Google Scholar
  128. 128.
    Xu G, Ma Y-F, Zhang H, Yang S (2002) Motion based event recognition using HMM. In: Proc. of IEEE ICPR’02, Quebec, Canada, pp 831–834Google Scholar
  129. 129.
    Xu C, Wang J, Li Y, Wan K, Duan L-Y (2006) Live sports event detection based on broadcast video and web-casting text. In: Proc. of ACM multimedia’06, Santa Barbara, CA, USA, pp 221–230Google Scholar
  130. 130.
    Xu M, Li J, Hu Y, Chia L-T, Lee B-S, Rajan D, Cai J (2006) An event-driven sports video adaptation for the MPEG-21 DIA framework. In: Proc of IEEE ICME’06, Canada, pp 1245–1248Google Scholar
  131. 131.
    Xu M, Li J, Chia L-T, Hu Y, Lee B-S, Rajan D, Jin JS (2006) Event on demand with MPEG-21 video adaptation system. In: Proc. of ACM multimedia’06, Santa Barbara, USA, pp 921–930Google Scholar
  132. 132.
    Ye Q, Huang Q, Gao W, Jiang S (2005) Exciting event detection in broadcast soccer video with mid-level description and incremental learning. In: Proc. of ACM Multimedia’05, Singapore, pp 455–458Google Scholar
  133. 133.
    Yokoi T, Fujiyoshi H (2006) Generating a time shrunk lecture video by event detection. In: Proc. of IEEE ICME’06, Canada, pp 641–644Google Scholar
  134. 134.
    Yoneyama A, Yeh CH, Kuo CCJ (2004) Robust traffic event extraction via content understanding for highway surveillance system. In: Proc. of IEEE ICME’04, Taipei, Taiwan, pp 1679–1682Google Scholar
  135. 135.
    Yoon K, DeMenthon D, Doermann DS (2000) Event detection from MPEG video in the compressed domain. In: Proc. of IEEE ICPR’00, Singapore, pp 1819–1822Google Scholar
  136. 136.
    Zhang D, Chang S-F (2002) Event detection in baseball video using superimposed caption recognition. In: Proc. of ACM multimedia’02, Juan Les Pins, France, pp 315–318Google Scholar
  137. 137.
    Zhang D, Gatica-Perez D, Bengio S (2005) Semi-supervised meeting event recognition with adapted HMMs. In: Proc. of IEEE ICME’05, Amsterdam, The Netherlands, pp 1102–1105Google Scholar
  138. 138.
    Zhang Z, Huang K, Tan T, Wang L (2007) Trajectory series analysis based event rule induction for visual surveillance. In: Proc. of IEEE CVPR’07, Minnesota, USAGoogle Scholar
  139. 139.
    Zelnik-Manor L, Irani M (2001) Event-based analysis of video. In: Proc. of IEEE CVPR’01, Hawaii, USA, pp 123–130Google Scholar
  140. 140.
    Zelnik-Manor L, Irani M (2006) Statistical analysis of dynamic actions. IEEE Trans Pattern Anal Mach Intell 28(9):1530–1535CrossRefGoogle Scholar
  141. 141.
    Zhang D, Gatica-Perez D, Bengio S, McCowan I (2005) Semi-supervised adapted HMMs for unusual event detection. In: Proc. of IEEE CVPR’05, San Diego, USA, pp 611–618Google Scholar
  142. 142.
    Zhong H, Shi J, Visontai M (2004) Detecting unusual activity in video. In: Proc of IEEE CVPR’04, Washington, DC, USA, pp 819–826Google Scholar
  143. 143.
    Zhou H, Kimber D (2004) Unusual event detection via multi-camera video mining. In: Proc. of IEEE ICVR’04, Cambridge, UK, pp 1161–1166Google Scholar
  144. 144.
    Zhu G, Huang Q, Xu C, Rui Y, Jiang S, Gao W, Yao H (2007) Trajectory based event tactics analysis in broadcast sports video. In: Proc. of ACM Multimedia’07, Augsburg, Germany, pp 58–67Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • WeiQi Yan
    • 1
  • Declan F. Kieran
    • 1
  • Setareh Rafatirad
    • 2
  • Ramesh Jain
    • 2
  1. 1.Institute of ECITQueen’s University BelfastBelfastUK
  2. 2.Department of Computer ScienceUniversity of CaliforniaIrvineUSA

Personalised recommendations