Abstract
Video event detection is an effective way to automatically understand the semantic content of the video. However, due to the mismatch between low-level visual features and high-level semantics, the research of video event detection encounters a number of challenges, such as how to extract the suitable information from video, how to represent the event, how to build up reasoning mechanism to infer the event according to video information. In this paper, we propose a novel event detection method. The method detects the video event based on the semantic trajectory, which is a high-level semantic description of the moving object’s trajectory in the video. The proposed method consists of three phases to transform low-level visual features to middle-level raw trajectory information and then to high-level semantic trajectory information. Event reasoning is then carried out with the assistance of semantic trajectory information and background knowledge. Additionally, to release the users’ burden in manual event definition, a method is further proposed to automatically discover the event-related semantic trajectory pattern from the sample semantic trajectories. Furthermore, in order to effectively use the discovered semantic trajectory patterns, the associative classification-based event detection framework is adopted to discover the possibly occurred event. Empirical studies show our methods can effectively and efficiently detect video events.
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Alvares LO, Bogorny V, Kuijpers B et al (2007) Mining associations between sets of items in massive databases. In: Proceedings of the 15th annual ACM international symposium on advances in geographic information systems, New York, NY, USA, pp 221–228
Chang S-F, Smith JR (1997) Visually searching the web for content. IEEE MultiMed 4(3): 12–20
Chang S-F, Chen W, Meng HJ et al (1998) A fully automated content-based video search engine supporting spatiotemporal queries. IEEE Trans Circuits Syst Video Technol 8(5): 602–615
Jung Y-K, Lee K-W, Ho Y-S (2001) Content-based event retrieval using semantic science interpretations for automated traffic surveillance. IEEE Trans Intell Transp Syst 2(3): 151–163
Tran MB, Manubhai TM (2008) Survey of vision-based trajectory learning and analysis for surveillance. IEEE Trans Circuits Syst Video Technol 18(8): 1114–1127
Ebadollahi S, Xie L, Chang S-F et al (2006) Visual event detection using multi-dimensional concept dynamics. In: IEEE ICME, pp 2169–2217
Xu D, Chang S-F (2008) Video event recognition using kernel methods with multilevel temporal alignment. IEEE Trans Pattern Anal Mach Intell 30(11): 1986–1997
Claudio P, Luca FG (2005) Trajectory clustering and its applications for video surveillance. In: Proceedings of IEEE international conference on advanced video based surveillance, pp 40–45
Pittore M, Basso C, Verri A (1999) Representing and recognizing visual dynamics events with support vector machines. In: Proceedings of 10th international conference on image analysis and processing, pp 18–23
Brand M, Oliver N, Pentland A (1997) Coupled hidden Markov models for complex action recognition. In: Proceedings of the 1997 conference on computer vision and pattern recognition (CVPR ’97), pp 994–999
Peursum P, Venkatesh S, West GAW et al (2003) Object labelling from human action recognition. In: Proceedings of the first IEEE international conference on pervasive computing and communications, pp 399–406
Shet VD, Harwood D, Davis LS (2005) VidMAP: video monitoring of activity with Prolog. In: Proceedings of AVSS’05, pp 224–229
Ghanem N, DeMenthon D, Doermann D et al (2004) Representation and recognition of events in surveillance video using petri nets. In: Proceedings of the 2004 conference on computer vision and pattern recognition workshop (CVPRW’04), Washington DC, USA, pp 112–120
Tran SD, Davis LS (2008) Event modeling and recognition using Markov logic networks. In: Proceedings of the 10th European conference on computer vision: Part II (ECCV ’08), Marseille, France, pp 112–120
Zhu G, Huang Q, Xu C et al (2007) Trajectory based event tactics analysis in broadcast sports video. In: Proceedings of the 15th international conference on multimedia (MM ’07), pp 58–67
Pelekis N, Kopanakis I, Kotsifakos EE et al (2011) Clustering uncertain trajectories. Knowl Inf Syst 28(1): 117–147
Claudio P, Luca FG (2006) On-line trajectory clustering for anomalous events detection. Pattern Recognit Lett 27(15): 1835–1842
Patino L, Benhadda H, Corvee E et al (2008) Extraction of activity patterns on large video recordings. Comput Vis IET 2(2): 108–128
Cao L, Ou Y, Yu PS (2011) Coupled behavior analysis with applications. IEEE Trans Knowl Data Eng 99 (preprint)
Hu K, Lu Y, Zhou L et al (2011) Integrating classification and association rule mining: a concept lattice framework. In: Proceedings of the 7th international workshop on new directions in rough sets, data mining, and granular-soft computing, London, UK, pp 443–447
Li W, Han J, Pei J (2001) CMAR: accurate and efficient classification based on multiple class-association rules. In: Proceedings of the 2001 IEEE international conference on data mining (ICDM ’01), Washington, DC, USA, pp 369–376
Stefano S, Christine P, Luisa DM et al (2008) A conceptual view on trajectories. Data Knowl Eng 65(1): 126–146
Perez G, Corcho O, Fernandez-Lopez M (2003) Ontological engineering: with examples from the areas of knowledge management, e-Commerce and the semantic web. Springer, Berlin
Fodeh S, Punch B, Tan P-N (2011) On ontology-driven document clustering using core semantic features. Knowl Inf Syst 28(2): 395–421
Francisco V, Gervas P, Peinado F (2010) Ontological reasoning for improving the treatment of emotions in text. Knowl Inf Syst 25(3): 421–443
Baader F, Calvanese D, McGuinness D et al (2003) The description logic handbook: theory, implementation, and applications. Cambridge University Press, Cambridge
Yan Z, Parent C, Spaccapietra S et al (2010) A hybrid model and computing platform for spatio-semantic trajectories. The semantic web: research and applications, lecture Notes in computer science, vol 6088, 60–75
Palma AT, Bogorny V, Kuijpers B et al (2008) A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM symposium on applied computing (SAC’08), pp 863–868
Alvares LO, Bogorny V, Kuijpers B et al (2007) A model for enriching trajectories with semantic geographical information. In: Proceedings of the 15th annual ACM international symposium on advances in geographic information systems (GIS ’07), New York, NY, USA, pp 221–228
Pei J, Han J, Mortazavi-Asl B (2004) Mining sequential patterns by pattern-growth: the PrefixSpan approach. IEEE Trans Knowl Data Eng 16: 1424–1440
Pei J, Han J, Mortazavi-Asl B (2001) PrefixSpan: mining sequential patterns by prefix-projected growth. In: Proceedings of the 17th international conference on data engineering, Washington DC, USA, pp 215–224
Yin X, Han J (2003) CPAR: classification based on predictive association rules. SDM, 2003
Vlachos M, Gunopoulos D, Kollios G (2002) Discovering similar multidimensional trajectories. In: Proceedings of the 18th international conference on data engineering (ICDE ’02), Washington DC, USA, pp 673–684
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Wang, X., Li, G., Jiang, G. et al. Semantic trajectory-based event detection and event pattern mining. Knowl Inf Syst 37, 305–329 (2013). https://doi.org/10.1007/s10115-011-0471-8
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DOI: https://doi.org/10.1007/s10115-011-0471-8