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Volumn , Issue , 2011, Pages 1164-1171

Gaussian process regression flow for analysis of motion trajectories

Author keywords

[No Author keywords available]

Indexed keywords

ACCELERATION CHANGE; ANOMALOUS EVENTS; CLASSES OF MOTION; COMPLEX MOTION; DATA SETS; FRAME RATE; GAUSSIAN PROCESS REGRESSION; LIMITED DATA; MOTION TRAJECTORIES; RANDOM SAMPLING; SPARSE SET; TRAFFIC MONITORING; VIDEO DATA;

EID: 84863025459     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126365     Document Type: Conference Paper
Times cited : (194)

References (25)
  • 1
    • 56749107053 scopus 로고    scopus 로고
    • Floor fields for tracking in high density crowd scenes
    • 6
    • S. Ali and M. Shah. Floor fields for tracking in high density crowd scenes. In Proc' of ECCV, pages 1-14, 2008. 6
    • (2008) Proc' of ECCV , pp. 1-14
    • Ali, S.1    Shah, M.2
  • 2
    • 31844438573 scopus 로고    scopus 로고
    • Gaussian process classification for segmenting and annotating sequences
    • 2
    • Y. Altun, T. Hofmann, and A. J. Smola. Gaussian process classification for segmenting and annotating sequences. In Proc' of ICML, pages 4-9, 2004. 2
    • (2004) Proc' of ICML , pp. 4-9
    • Altun, Y.1    Hofmann, T.2    Smola, A.J.3
  • 4
    • 33750317347 scopus 로고    scopus 로고
    • Preference learning with gaussian processes
    • W. Chu and Z. Ghahramani. Preference learning with gaussian processes. In Proc' of the ICML, 2005. 3
    • (2005) Proc' of the ICML , pp. 3
    • Chu, W.1    Ghahramani, Z.2
  • 7
  • 8
    • 77951168145 scopus 로고    scopus 로고
    • Classifying spatiotemporal object trajectories using unsupervised learning of basis function coefficients
    • 2
    • S. Khalid and A. Naftel. Classifying spatiotemporal object trajectories using unsupervised learning of basis function coefficients. In Proc' of the third ACM international workshop on VSSN, pages 45-52, 2005. 2
    • (2005) Proc' of the third ACM international workshop on VSSN , pp. 45-52
    • Khalid, S.1    Naftel, A.2
  • 9
    • 34047211509 scopus 로고    scopus 로고
    • A coarse-to-fine strategy for vehicle motion trajectory clustering
    • 2
    • X. Li, W. Hu, and W. Hu. A coarse-to-fine strategy for vehicle motion trajectory clustering. In Proc' of the ICPR '06, pages 591-594, 2006. 2
    • (2006) Proc' of the ICPR'06 , pp. 591-594
    • Li, X.1    Hu, W.2    Hu, W.3
  • 11
    • 84863069746 scopus 로고    scopus 로고
    • Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes
    • 1, 2
    • B. Morris and M. Trivedi. Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In Proc'IEEE CVPR '09, pages 1-8, 2009. 1, 2
    • (2009) Proc' IEEE CVPR '09 , pp. 1-8
    • Morris, B.1    Trivedi, M.2
  • 13
    • 78149473217 scopus 로고    scopus 로고
    • Unsupervised learning of activities in video using scene context
    • 6
    • S. Oh and A. Hoogs. Unsupervised learning of activities in video using scene context. In Proc' of ICPR, pages 3579- 3582, 2010. 6
    • (2010) Proc' of ICPR , pp. 3579-3582
    • Oh, S.1    Hoogs, A.2
  • 14
    • 33748453850 scopus 로고    scopus 로고
    • On-line trajectory clustering for anomalous events detection
    • 1, 5
    • C. Piciarelli and G. L. Foresti. On-line trajectory clustering for anomalous events detection. Pattern Recogn. Lett., 27:1835-1842, 2006. 1, 5
    • (2006) Pattern Recogn. Lett. , vol.27 , pp. 1835-1842
    • Piciarelli, C.1    Foresti, G.L.2
  • 18
    • 33845583192 scopus 로고    scopus 로고
    • 3d people tracking with gaussian process dynamical models
    • 2
    • R. Urtasun, D. J. Fleet, and P. Fua. 3d people tracking with gaussian process dynamical models. In Proc' of CVPR, pages 238-245, 2006. 2
    • (2006) Proc' of CVPR , pp. 238-245
    • Urtasun, R.1    Fleet, D.J.2    Fua, P.3
  • 19
    • 84863075804 scopus 로고    scopus 로고
    • US AIR FORCE. CLIF, 6
    • US AIR FORCE. CLIF https://www.sdms.afrl.af.mil/. 6
  • 20
    • 0036211177 scopus 로고    scopus 로고
    • Discovering similar multidimensional trajectories
    • 1, 3
    • M. Vlachos, D. Gunopoulos, and G. Kollios. Discovering similar multidimensional trajectories. In Proc' ICDE, pages 673-680, 2002. 1, 3
    • (2002) Proc' ICDE , pp. 673-680
    • Vlachos, M.1    Gunopoulos, D.2    Kollios, G.3
  • 22
    • 84863029203 scopus 로고    scopus 로고
    • 6
    • WWW. http://www.justin.tv/adamsblock. 6
  • 25
    • 34147174850 scopus 로고    scopus 로고
    • Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes
    • 1, 2
    • Z. Zhang, K. Huang, and T. Tan. Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In Proc' of ICPR '06, pages 1135-1138, 2006. 1, 2
    • (2006) Proc' of ICPR ' 06 , pp. 1135-1138
    • Zhang, Z.1    Huang, K.2    Tan, T.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.