Video analytics for business intelligence

Studies in Computational Intelligence

Volumn 409, Issue , 2012, Pages 309-354

  Hakeem, Asaad ^a   Gupta, Himaanshu ^a   Kanaujia, Atul ^a   Choe, Tae Eun ^a   Gunda, Kiran ^a   Scanlon, Andrew ^a   Yu, Li ^a   Zhang, Zhong ^a   Venetianer, Peter ^a   Rasheed, Zeeshan ^a   Haering, Niels ^a  

^a OBJECTVIDEO INC   (United States)

Author keywords

3D object modeling; Automated video analysis; business intelligence; calibration; complex event detection; context; geo registration; image and video understanding; pixel to text; retrieval; search

Indexed keywords

EID: 84860521964     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-28598-1_10     Document Type: Article

References (42)

1. Di Battista, G., Eades, P., Tamassia, R., Tollis, I. G.: Graph Drawing: Algorithms for the Visualization of Graphs. Prentice-Hall (1999)
2. Collins, R., Lipton, A., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., Hasegawa, O., Burt, P., Wixson, A.: A System for Video Surveillance and Monitoring. CMU Technical Report CMU-RI-TR-00-12 (2000)
3. Isard, M., Blake, A.: CONDENSATION-conditional density propagation for visual tracking. IJCV 29(1), 5-28 (1998) (Pubitemid 128523364)
4. Rosales, R., Sclaroff, S.: Improved Tracking of Multiple Humans with Trajectory Prediction and Occlusion Modeling. In: IEEE Conf. On Computer Vision and Pattern Recognition, Santa Barbara, CA (1998)
5. Wren, C., Azarbayejani, A., Darrel, T., Pentland, A.: Pfinder: Real-time tracking of the human body. In: Proc. 2nd Int. Conf. on Automatic Face and Gesture Recognition, pp. 51-56 (1996)
6. Isard, M., MacCormick, J.: BraMBLe: A Bayesian multiple-blob tracker. In: Proc. ICCV (2001)
7. Isard, M., Blake, A.: ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework. In: Burkhardt, H.-J., Neumann, B. (eds. ) ECCV [1998]. LNCS, vol. 1406, pp. 893-908. Springer, Heidelberg (1998) (Pubitemid 128093126)
8. Viola, Jones: Robust Real-time Object Detection. In: IJCV (2001)
9. Kanaujia, A., Sminchisescu, C., Metaxas, D.: Semi-supervised Hierarchical Models for 3D Human Pose Reconstruction. In: Computer Vision and Pattern Recognition (2007)
10. Sminchisescu, C., Kanaujia, A., Metaxas, D.: BM3E: Discriminative Density Propagation for Visual Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(11), 2030-2044 (2007) (Pubitemid 47423794)
11. Gilks, W., Richardson, S., Spiegelhalter, D.: Markov Chain Monte Carlo in Practice. Chapman and Hall (1996)
12. Lee, M., Cohen, I.: Proposal Maps driven MCMC for Estimating Human Body Pose in Static Images. In: Proc. Computer Vision and Pattern Recognition, pp. 334-341 (2004)
13. Lee, M., Nevatia, R.: Dynamic Human Pose Estimation using Markov chain Monte Carlo Approach. In: MOTION, pp. 168-175 (2005)
14. CAESAR, Civilian American and European Surface Anthropometry Resource Project (2002)
15. Sun, H., Goswami, A., Metaxas, D., Bruckner, J.: Cyclogram planarity is preserved in upward slope walking. In: Proc. International Society of Biomechanics XVII Congress, Calgary, Canada (1999)
16. Grasso, R., Bianchi, L., Lacquaniti, F.: Motor patterns for human gait: Backward versus forward locomotion. J. Neurophysiology 80, 1868-1885 (1998) (Pubitemid 28489118)
17. Borghese, A., Bianchi, L., Lacquaniti, F.: Kinematic Determinants of Human Locomotion. J. Physiology (494), 863-879 (1996)
18. Guo, Y., et al.: Matching Vehicles under Large Pose Transformations using approximate 3D. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (2008)
19. Leotta, M. J., Mundy, J. L.: Predicting high resolution image edges with a generic, adaptive, 3-D vehicle model. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2009)
20. Lou, J., Tan, T.: 3-D Model-Based Vehicle Tracking. IEEE Transactions on PAMI (2005)
21. Zhu, S. C., Mumford, D. B.: Quest for a stochastic grammar of images. In: Foundations and Trends of Computer Graphics and Vision (2006)
22. Tu, Z. W., Zhu, S. C.: Parsing images into regions, curves and curve groups. IJCV 69(2), 223-249 (2006)
23. Babenko, B., Yang, M. H., Belongie, S.: Visual Tracking with Online Multiple Instance Learning. In: CVPR (2009)
24. Chen, K. W., Lai, C. C., Hung, Y. P., Chen, C. S.: An adaptive learning method for target tracking across multiple cameras. In: CVPR (2008)
25. Dietterich, T. G., Lathrop, R. H., Lozano-Perez, T.: Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence 89, 31-71 (1997) (Pubitemid 127412230)
26. Javed, O., Shafique, K., Shah, M.: Appearance modeling for tracking in multiple nonoverlapping cameras. In: CVPR 2005 (2005)
27. Porikli, F.: Inter-camera color calibration by correlation model function. In: ICIP 2003 (2003)
28. Viola, P., Platt, J. C., Zhang, C.: Multiple instance boosting for object detection. In: NIPS 2005 (2005)
29. Moore, D., Essa, I.: Recognizing Multitasked Activities using Stochastic Context-Free Grammar. In: CVPR 2001 (2001)
30. Earley, J. C.: An Efficient Context-Free Parsing Algorithm. PhD thesis, Carnegie-Mellon University (1968)
31. Jhuang, H., et al.: A Biologically Inspired System for Action Recognition. In: ICCV 2007 (2007)
32. Langkilde-Geary, I., Knight, K.: HALogen Input Representation, http://www. isi. edu/publications/licensed-sw/halogen/interlingua. html
33. Knight, K.: Unification: A Multidisciplinary Survey. ACM Computing Surveys 21(1) (1989)
34. Pollard, C., Sag, I. A.: Head-Driven Phrase Structure Grammar. University of Chicago Press, Chicago (1994)
35. Rasheed, Z., Cao, X., Shafique, K., Liu, H., Yu, L., Lee, M., Ramnath, K., Choe, T., Javed, O., Haering, N.: Automated Visual Analysis in Large Scale Sensor Networks. In: ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC) (September 2008)
36. jQuery. JavaScript library, http://www. jquery. com
37. JSON, http://www. json. org
38. Peres, R., Pedreira, C.: Generalized risk zone: Selecting observations for classification. IEEE PAMI 31(7) (2009)
39. Gray, D., Tao, H.: Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features. In: Forsyth, D., Torr, P., Zisserman, A. (eds. ) ECCV 2008, Part I. LNCS, vol. 5302, pp. 262-275. Springer, Heidelberg (2008)
40. Kuhn, H. W.: The Hungarian Method for the assignment problem. Naval Research Logistics Quarterly 2, 83-97 (1955)
41. Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE) 24(5), 603-619 (2002) (Pubitemid 34550429)
42. Dukette, D., Cornish, D.: The Essential 20: Twenty Components of an Excellent Health Care Team, pp. 72-73. RoseDog Books (2009)