Large-scale situation awareness with camera networks and multimodal sensing

Proceedings of the IEEE

Volumn 100, Issue 4, 2012, Pages 878-892

  Ramachandran, Umakishore ^a   Hong, Kirak ^a   Iftode, Liviu ^b   Jain, Ramesh ^c   Kumar, Rajnish ^a,d   Rothermel, Kurt ^e   Shin, Junsuk ^a   Sivakumar, Raghupathy ^f  

^a Georgia Institute of Technology   (United States)

^b RUTGERS UNIVERSITY   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d Weyond   (United States)

^e UNIVERSITY OF STUTTGART   (Germany)

^f GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Large scale distributed systems; programing model; resource management; scalability; situation awareness; video based surveillance

Indexed keywords

ABSTRACTING; CAMERAS; EMERGENCY SERVICES; SCALABILITY;

LARGE-SCALE DISTRIBUTED SYSTEM; MULTIMEDIA ALGORITHMS; RESOURCE MANAGEMENT; RESOURCE REQUIREMENTS; SENSOR-BASED APPLICATIONS; SITUATION AWARENESS; TECHNOLOGICAL ADVANCES; VIDEO BASED SURVEILLANCE;

SECURITY SYSTEMS;

EID: 84863419856     PISSN: 00189219     EISSN: None     Source Type: Journal    
DOI: 10.1109/JPROC.2011.2182093     Document Type: Conference Paper

References (63)

1. M. McCahill and C. Norris, "Estimating the extent, sophistication and legality of CCTV in London," in CCTV, M. Gill, Ed. London, U.K.: Palgrave Macmillan, 2003.
2. R. Hunter, Chicago's Surveillance Plan is an Ambitious Experiment, Gartner Research, 2004. [Online]. Available: http://www. gartner.com/ DisplayDocument?doc-cd= 123919.
3. C. Norris, M. McCahill, and D. Wood, "The growth of CCTV: A global perspective on the international diffusion of video surveillance in publicly accessible space," Surveill. Soc., vol. 2, no. 2/3, pp. 110-135, 2004.
4. W. E. L. Grimson and C. Stauffer, "Adaptive background mixture models for real-time tracking," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 1999, DOI: 10.1109/CVPR. 1999.784637.
5. A. Elgammal, D. Harwood, and L. S. Davis, "Nonparametric background model for background subtraction," in Proc. 6th Eur. Conf. Comput. Vis., pp. 751-767, 2000.
6. D. M. Gavrila, "Pedestrian detection from a moving vehicle," in Proc. 6th Eur. Conf. Comput. Vis. II, 2000, pp. 37-49.
7. P. A. Viola and M. J. Jones, "Robust real-time face detection," Int. J. Comput. Vis., vol. 57, no. 2, pp. 137-154, 2004.
8. I. Haritaoglu, D. Harwood, and L. S. Davis, "W4: Who? when? where? what? a real time system for detecting and tracking people," in Proc. IEEE Int. Conf. Autom. Face Gesture Recognit., 1998, pp. 222-227.
9. C. R. Wern, A. Azarbayejani, T. Darrell, and A. P. Pentland, "Pfinder: Real-time tracking of human body," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 780-785, Jul. 1997. (Pubitemid 127762271)
10. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, "Face recognition: A literature survey," ACM Comput. Surv., vol. 35, no. 4, pp. 399-458, 2003.
11. J. Little and J. Boyd, "Recognizing people by their gait: The shape of motion," Videre, vol. 1, no. 2, pp. 1-32, 1998.
12. A. Bissacco, A. Chiuso, Y. Ma, and S. Soatto, "Recognition of human gaits," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2001, vol. 2, pp. 52-58.
13. D. M. Gavrila, "The visual analysis of human movement: A survey," Comput. Vis. Image Understand., vol. 73, no. 1, pp. 82-98, 1999.
14. A. F. Bobick and J. W. Davis, "The recognition of human movement using temporal templates," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 3, pp. 257-267, Mar. 2001. (Pubitemid 32286632)
15. T. B. Moeslund and E. Granum, "A survey of computer vision-based human motion capture," Comput. Vis. Image Understand., vol. 81, no. 3, pp. 231-268, 2001. (Pubitemid 32415976)
16. IBM Smart Surveillance System (S3). [Online]. Available: http://www.research.ibm.com/peoplevision/
17. Video Surveillance Integrated Surveillance Systems. [Online]. Available: https://www.buildingtechnologies.siemens.com
18. Products That Make Surveillance Smart. [Online]. Available: http://www. objectvideo.com/products/
19. P. F. Felzenszwalb, R. B. Girshick, D. A. McAllester, and D. Ramanan, "Object detection with discriminatively trained part-based models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp. 1627-1645, Sep. 2010.
20. X. Wang, T. X. Han, and S. Yan, "An HOG-LBP human detector with partial occlusion handling," in Proc. Int. Conf. Comput. Vis., 2009, pp. 32-39.
21. A. Yilmaz, O. Javed, and M. Shah, "Object tracking: A survey," ACM Comput. Surv., vol. 38, no. 4, p. 13, 2006.
22. B. Babenko, M. Yang, and S. J. Belongie, "Visual tracking with online multiple instance learning," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2009, pp. 983-990.
23. D. A. Ross, J. Lim, R. Lin, and M. Yang, "Incremental learning for robust visual tracking," Int. J. Comput. Vis., vol. 77, pp. 125-141, 2008. (Pubitemid 351294740)
24. J. Kwon and K. M. Lee, "Visual tracking decomposition," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2010, pp. 1269-1276.
25. A. Basharat, A. Gritai, andM. Shah, "Learning object motion patterns for anomaly detection and improved object detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2008, DOI: 10.1109/CVPR.2008.4587510.
26. I. Saleemi, K. Shafique, and M. Shah, "Probabilistic modeling of scene dynamics for applications in visual surveillance," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 8, pp. 1472-1485, Aug. 2009.
27. B. Yao and L. Fei-Fei, "Modeling mutual context of object and human pose in human-object interaction activities," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2010, pp. 17-24.
28. V. Mahadevan, W. Li, V. Bhalodia, and N. Vasconcelos, "Anomaly detection in crowded scenes," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2010, pp. 1975-1981.
29. X. Wang, X. Ma, and W. Grimson, "Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 3, pp. 539-555, Mar. 2009.
30. H. Zhong, J. Shi, and M. Visontai, "Detecting unusual activity in video," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2004, vol. 2, pp. 819-826.
31. I. Bravo, J. Balias, A. Gardel, J. L. Lzaro, F. Espinosa, and J. Garca. (2011). Efficient smart CMOS camera based on FPGAs oriented to embedded image processing. Sensors [Online]. 11(3), pp. 2282-2303. Available: http://www.mdpi.com/1424-8220/11/3/2282/
32. S. K. Nayar, "Computational cameras: Redefining the image," IEEE Comput. Mag., vol. 39, Special Issue on Computational Photography, no. 8, pp. 30-38, Aug. 2006. (Pubitemid 44275789)
33. P. Chen, P. Ahammad, C. Boyer, S. Huang, L. Lin, E. Lobaton, M. Meingast, S. Oh, S. Wang, P. Yan, A. Y. Yang, C. Yeo, L. Chung Chang, J. D. Tygar, and S. S. Sastry, "Citric: A low-bandwidth wireless camera network platform," in Proc. ACM/IEEE Int. Conf. Distrib. Smart Cameras, 2008, DOI: 10.1109/ICDSC.2008.4635675.
34. P. Kulkarni, D. Ganesan, P. Shenoy, and Q. Lu, "Senseye: A multi-tier camera sensor network," in Proc. 13th Annu. ACM Int. Conf. Multimedia, 2005, pp. 229-238.
35. H. Broers, W. Caarls, P. Jonker, and R. Kleihorst, "Architecture study for smart cameras," in Proc. EOS Conf. Ind. Imag. Mach. Vis., 2005, pp. 39-49.
36. Texas Instruments IP Camera. [Online]. Available: http://www.ti.com/ ipcamera
37. Axis Communications. [Online]. Available: http://www.axis.com/
38. Microsoft Kinect. [Online]. Available: http://www.xbox.com/kinect
39. IEEE Standard for Wireless Local Area Networks, 802.11n. [Online]. Available: http://www.ieee802.org/11.
40. The Zigbee Specification. [Online]. Available: http://www.zigbee.org
41. C. Perkins, E. B. Royer, and S. Das, "Ad hoc on-demand distance vector (AODV) routing," IETF RFC 3561, 2003.
42. 3GPP Long Term Evolution. [Online]. Available: http://www.3gpp.org/ article/lte
43. Worldwide Interoperability for Microwave Access. [Online]. Available: http://www. wimaxforum.org
44. Xbow Micaz Motes. [Online]. Available: http://www.xbow.com
45. RPL: IPv6 Routing Protocol for Low Power and Lossy Networks, draft-ietf-roll-rpl-19. [Online]. Available: http://tools.ietf.org/html/draft- ietf-roll-rpl-19
46. RFC 3344: IP Mobility Support for IPv4, U.S., 2002, Tech. Rep.
47. IBMA Smarter Planet. [Online]. Available: http://www.ibm.com/ smarterplanet.
48. L. Luo, A. Kansal, S. Nath, and F. Zhao, "Sharing and exploring sensor streams over geocentric interfaces," in Proc. 16th ACM SIGSPATIAL Int. Conf. Adv. Geograph. Inf. Syst., Irvine, CA, Nov. 2008, pp. 3-12.
49. Nokia, Sensor Planet. [Online]. Available: http://www.sensorplanet.org/.
50. L. Sanchez, J. Lanza, M. Bauer, R. L. Olsen, and M. G. Genet, "A generic context management framework for personal networking environments," in Proc. 3rd Annu. Int. Conf. Mobile Ubiquitous Syst., 2006, DOI: 10.1109/MOBIQW.2006.361743.
51. S. Kang, J. Lee, H. Jang, H. Lee, Y. Lee, S. Park, T. Park, and J. Song, "Seemon: Scalable and energy-efficient context monitoring framwork for sensor-rich mobile environments," in Proc. ACM Int. Conf. Mobile Syst., 2008.
52. D. J. Lillethun, D. Hilley, S. Horrigan, and U. Ramachandran, "MB++: An integrated architecture for pervasive computing and high-performance computing," in Proc. 13th IEEE Int. Conf. Embedded Real-Time Comput. Syst. Appl., Aug. 2007, pp. 241-248.
53. F. Hohl, U. Kubach, A. Leonhardi, K. Rothermel, and M. Schwehm, "Next century challenges: NexusVAn open global infrastructure for spatial-aware applications," in Proc. 5th ACM/IEEE Int. Conf. Mobile Comput. Netw., Seattle, WA, Aug. 1999, pp. 249-255.
54. R. Lange, N. Cipriani, L. Geiger, M. Grossmann, H. Weinschrott, A. Brodt, M. Wieland, S. Rizou, and K. Rothermel, "Making the world wide space happen: New challenges for the nexus platform," in Proc. 7th IEEE Int. Conf. Pervasive Comput. Commun., 2009, DOI: 10.1109/PERCOM. 2009.4912782.
55. B. Gedik, H. Andrade, K.-L. Wu, P. S. Yu, and M. Doo, "Spade: The system S declarative stream processing engine," in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2008, pp. 1123-1134. [Online]. Available: http://doi.acm.org/10.1145/1376616.1376729.
56. L. Neumeyer, B. Robbins, A. Nair, and A. Kesari, "S4: Distributed stream computing platform," in Proc. IEEE Int. Conf. Data Mining Workshops, 2010, pp. 170-177.
57. W. Thies, M. Karczmarek, and S. P. Amarasinghe, "Streamit: A language for streaming applications," in Proc. 11th Int. Conf. Compiler Construct., London, U.K., 2002, pp. 179-196. [Online]. Available: http://portal.acm.org/citation.cfm?id= 647478.727935.
58. P. S. Pillai, L. B. Mummert, S. W. Schlosser, R. Sukthankar, and C. J. Helfrich, "Slipstream: Scalable low-latency interactive perception on streaming data," in Proc. 18th Int. Workshop Netw. Oper. Syst. Support Digital Audio Video, 2009, pp. 43-48. [Online]. Available: http://doi.acm.org/ 10.1145/1542245.1542256.
59. K. Hong, B. Branzoi, J. Shin, S. Smaldone, L. Iftode, and U. Ramachandran, Target Container: A Target-Centric Parallel Programming Abstraction for Video-Based Surveillance, 2010. [Online]. Available: http://hdl.handle.net/1853/36186.
60. J. Shin, R. Kumar, D. Mohapatra, U. Ramachandran, and M. Ammar, "ASAP: A camera sensor network for situation awareness," in OPODIS 2007, vol. 4878. Berlin, Germany: Springer-Verlag, 2007, pp. 31-47, ser. Lecture Notes in Computer Science.
61. R. Feris, A. Hampapur, Y. Zhai, R. Bobbitt, L. Brown, D. Vaquero, Y. Tian, H. Liu, and M.-T. Sun, "Casestudy: IBM smart surveillance system," in Intelligent Video Surveillance: Systems and Technologies, Y. Ma and G. Qian, Eds. London, U.K.: Taylor & Francis/CRC Press, 2009.
62. ABC News, ABC7 Puts Video Analytics to the Test, Feb. 23, 2010. [Online]. Available: http://abclocal.go.com/wls/story?section= news/special- segments&id=7294108.
63. A. Hampapur, L. Brown, J. Connell, A. Ekin, N. Haas, M. Lu, H. Merkl, S. Pankanti, A. Senior, C.-F. Shu, and Y. L. Tian, "Smart video surveillance: Exploring the concept of multiscale spatiotemporal tracking," IEEE Signal Process. Mag., vol. 22, no. 2, pp. 38-51, Mar. 2005.