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RESEARCH PROGRAM

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Similar research programs:
Scientific and education field classification
International Patent Classification
  • PHYSICS
    • SIGNALLING (indicating or display devices per se G09F; transmission of pictures H04N) [C9504]
      • SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS (signalling arrangements on vehicles B60Q, B62D41/00; railway signalling systems or devices B61L; on cycles B62J3/00, B62J6/00; safes or strong-rooms with alarm devices E05G; signalling or alarm devices in mines E21F17/18; lamps or shutters therefor F21; sensitive measuring elements, see the appropriate subclasses of G01; traffic control systems G08G; visual indicating means G09; sound-producing devices G10; radio or near-field calling systems H04B5/00, H04B7/00; selecting arrangements H04Q7/00, H04Q9/00; loudspeakers, microphones, gramophone pick-ups or like acoustic electromechanical transducers H04R) [C9504]
Geographical classification
Bibliografia
[1]O. Lanz, “Approximate Bayesian Multibody Tracking”, IEEE Trans on PAMI (in Press).
[2]S. Khan, M. Shah. Consistent labeling of tracked objects in multiple cameras with overlapping fields of view. IEEE Trans. on PAMI, 25(10):1355-1360, 2003.
[3]S. Calderara, R. Vezzani, A. Prati, R. Cucchiara. Entry edge of field of view for multi-camera tracking in distributed video surveillance. Proc. of IEEE Int. Conf. on AVSS, 93-98, 2005.
[4]R. Cucchiara, C. Grana, M. Piccardi, A. Prati, "Detecting Moving Objects, Ghosts and Shadows in Video Streams", IEEE Trans. on PAMI, 25(10): 1337-1342, 2003.
[5]J. Krumm, S. Harris, B. Meyers, B. Brumitt, M. Hale, S. Shafer, “Multi-camera multi-person tracking for easyliving” Proc.of IEEE Intl Workshop on Visual Surveillance, 3-10, 2000.
[6]M. Piccardi, E.D. Cheng, "Track matching over disjoint camera views based on an incremental major color spectrum histogram," Proc. of the IEEE Conference on AVSS, 147- 152, 2005.
[7]J. Kang, I. Cohen, G. Medioni, "Continuous tracking within and across camera streams". Proc. of IEEE Int'l Conf. on CVPR, Vol 1, 267-272, 2003.
[8]S.L. Dockstader , A.M. Tekalp. “Multiple camera tracking of interacting and occluded human motion”, Proc. of the IEEE, 89(10):1441-1455, 2001.
[9]A. Prati, F. Seghedoni, R. Cucchiara, "Fast Dynamic Mosaicing and Person Following", Proc. of ICPR 2006, (in Press).
[10]K. Lee, S. Ryu, S. Lee, K. Park. "Motion based object tracking with mobile camera." Electronics Letters, 34(3):256-258, 1998.
[11]J. P. Barreto, J. Batista, H. Araujo, "Model Predictive Control to Improve Visual Control of Motion: Applications in Active Tracking of Moving Targets," Proc. of 15th ICPR, 2000.
[12]I. Reid, D. Murray, "Active tracking of foveated feature clusters using affine structure." IJCV, Vol 18, 1996.
[13]B. Tordoff, D. Murray. "Reactive control of zoom while fixating using perspective and affine cameras." IEEE Trans on PAMI, Vol 26, No 1, 2004.
[14]L. de Agapito, R. Hartley, E. Hayman. "Linear calibration of a rotating and zooming camera." Proc. of CVPR, 1999.
[15]S. Sinha, M. Pollefeys. "Towards Calibrating a Pan-Tilt-Zoom Cameras Network." Proc. of OMNIVIS 2004.
[16]C.R. Wren, M. Erdem, A.J. Azarbayejani, "Automatic Pan-Tilt-Zoom Calibration in the Presence of Hybrid Sensor Networks", ACM International Workshop on VSSN,113-120, 2005.
[17]C. J. Costello, C. P. Diehl, A. Banerjee, H. Fisher, "Scheduling an active camera to observe people.", Proc. of VSSN 2004, 2004.
[18]A. Del Bimbo, F. Pernici, "Distant Targets Identification as an On-Line Dynamic Vehicle Routing Problem using an Active-Zooming Camera", Proc. of Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005.
[19]M. Ortolani, L. Gatani, G. Lo Re, A. Urso, S. Gaglio. “An efficient retransmission strategy for data gathering in wireless sensor networks”, Proc. of IEEE ETFA05, 2005.
[20]J. Aldrige, C. Gilbert. “Testing on CCTV perimeter surveillance systems. PSDB Publication, (14), 1995.
[21]M. Langheinrich, "A Privacy Awareness System for Ubiquitous Computing Environments." In: Gaetano Borriello, Lars Erik Holmquist (Eds.): 4th International Conference on Ubiquitous Computing (Ubicomp 2002), LNCS No. 2498, Springer-Verlag,.237-245, 2002.
[22]J. W. Patton, "Protecting Privacy in Public: Surveillance Technologies and the Value of Public Places." Ethics and Information Technology 2:181-187, 2000.
[23]A.W. Senior, S. Pankanti, A. Hampapur, L. Brown, Y-L Tian, A. Ekin. "Blinkering Surveillance: Enabling Video Privacy through Computer Vision." IBM Technical Report RC22886, 2003.
[24]R. Cucchiara , A. Prati, R. Vezzani , “A System for Automatic Face Obscuration for Privacy Purposes.” Pattern Recognition Letters. (in Press)
[25]P. Viola, M. Jones, "Rapid object detection using a boosted cascade of simple features." Proc of CVPR, 2001.
[26]M. Yang, D. Kriegman, N. Ahuja, "Detecting faces in images: A survey". IEEE Trans on PAMI, 24(1):34-58, 2002.
[27]E. Hjelm, B. Low, "Face detection: A survey", CVIU, 83(3):236-274, 2001.
[28]D. DeCarlo, D. Metaxas, "Optical Flow Constraints on Deformable Models with Applications to Face Tracking.", IJCV, 38(2), 99-127, 2000.
[29]R. Cucchiara, A. Prati, R. Vezzani, "Advanced Video Surveillance with Pan Tilt Zoom Cameras”. Proc. of Workshop on Visual Surveillance (VS) at ECCV 2006, 2006.
[30]X. Wu, Y. Ou, H. Qian, Y. Xu, "A detection system for human abnormal behavior," in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005. (IROS 2005), pp. 1204- 1208, 2005.
[31]R. Cucchiara, C. Grana, A. Prati, R. Vezzani, "Probabilistic Posture Classification for Human Behaviour Analysis" IEEE Trans on Systems, Man, and Cybernetics, Part A: Systems and Humans, vol 35, n. 1, 42-54, 2005.
[32]F. Cupillard, F. Bremond, M. Thonnat, "Behaviour recognition for individuals, groups of people and crowd," Intelligence Distributed Surveillance Systems, IEE Symposium on (Ref. No. 2003/10062) , 7/1- 7/5, 2003.
[33]S. Park, J. K. Aggarwal, "Semantic-level Understanding of Human Actions and Interactions using Event Hierarchy,"CVPRW 2004, Vol 1, 12, 2004
[34]M. Bertozzi, A. Broggi, A. Fascioli, A. Tibaldi, R. Chapuis, F. Chausse, "Pedestrian localization and tracking system with Kalman filtering," IEEE Intelligent Vehicles Symposium, 584- 589, 2004.
[35]F. Cupillard, F. Bremond, M. Thonnat, "Group behavior recognition with multiple cameras", Proc. of IEEE WACV, pp 177-183, 2002.
[36]D. Haussler, “Convolution Kernels on Discrete Structures”, University of California Santa Cruz, Technical Report UCSC-CRL-99-10, 1999.
[37]Z. Zhang, "Mining Surveillance Video for Independent Motion Detection," Second IEEE International Conference on Data Mining, 741, 2002.
[38]Alberto Del Bimbo, "Visual information retrieval”, Morgan Kaufmann Publishers Inc. 1-55860-624-6. 1999.
[39]C. Stauffer, E. Grimson, "Learning Patterns of Activity Using Real-Time Tracking", IEEE Trans on PAMI, 22(8):747-757, 2000.
[40]D. Buzan, S. Sclaroff, George Kollios, "Extraction and Clustering of Motion Trajectories in Video." Proc. of ICPR, Vol 2, 521-524, 2004.
[41]G. Doretto, E. Jones, S. Soatto. "Spatially homogeneous dynamic textures." Proc. ECCV, 2004.
[42]I. Haritaoglu, D. Harwood, L.S. Davis. “W4: real-time surveillance of people and their activities” IEEE Trans on PAMI, 22(8):809–830, 2000.
[43]N.M. Oliver, B. Rosario, A.P. Pentland, “Bayesian computer vision system for modeling human interactions” IEEE Trans on PAMI, 22(8):831–843, 2000.
[44]R. Cucchiara, A. Prati, R. Vezzani, L. Benini, E. Farella, P. Zappi, "An Integrated Multi-Modal Sensor Network for Video Surveillance”, Journal of Ubiquitous Computing and Intelligence, 2006 (in Press).
[45]E. Ardizzone, M. La Cascia, G. Lo Re, M. Ortolani, “An Integrated Architecture for Surveillance and Monitoring in an Archaeological Site”, 3rd ACM International Workshop on VSSN, 79-86, 2005.
[46]R. Cucchiara, A. Prati, L. Benini, E. Farella, "T_PARK: Ambient Intelligence for Security in Public Parks" Proc. of IEE International Workshop on IE, 243-251, 2005.
[47]P. Zappi, E. Farella, L. Benini "A PIR based wireless sensor node prototype for surveillance applications" Proc. of European Workshop on Wireless Sensor Networks, 2006.
[48]Y. Wang, E.Y. Chang, K.P. Cheng, “A video analysis framework for soft biometry security surveillance.” Proc. of ACM VSSN 2005.
[49]R. Cucchiara, "Multimedia Surveillance Systems" Proc. of ACM VSSN, 3-10, 2005.
[50]M. La Cascia, S. Sclaroff, V. Athitsos, "Fast, Reliable Head Tracking under Varying Illumination: An Approach Based on Registration of Texture-Mapped 3D Models," IEEE Trans. on PAMI, 22(4), 322-336, 2000.
[51]A. Prati, I. Mikic, M.M. Trivedi, R. Cucchiara, "Detecting Moving Shadows: Algorithms and Evaluation" IEEE Trans. on PAMI, 25(7), 918-923, 2003.
[52]W. Nunziati, J. Alon, S. Sclaroff, A. del Bimbo, “View registration using interesting segments of planar trajectories”, Proc. IEEE Conf. on AVSS 2005, 75–80, 2005.
Keywords
COMPUTER VISION, AUTOMATIC VIDEO SURVEILLANCE, PATTERN RECOGNITION, MULTICAMERA PEOPLE TRACKING, PEOPLE DETECTION AND RECOGNITION, EVENT DETECTION, ACTIVE CAMERAS, WIRELESS SENSOR NETWORKS, LOGICAL REASONING

FREE SURF: FREE SUrveillance in a pRivacy-respectFul way

Università degli Studi di Modena e Reggio Emilia
Abstract
Free surf is meant to be a paradigm for the new generation of video surveillance systems, free from controls by human operators, and completely respectful of the privacy. The technological support will be given by emerging solutions of computer engineering both in system architectures for real-time video processing and in innovative techniques of Computer Vision and Pattern Recognition. The majority of the commercial systems only focus on video acquisition and on their visualization in control rooms. The most innovative systems already exploit simple computer vision techniques for motion detection, but with many structural and technological constraints, such as the installation of fixed cameras only, with manual calibration, very simple target models, and, in particular, the lack of inferential capabilities and scene understanding that makes necessary the constant presence of human operators. The “free surveillance” systems aim at overcome these technological constraints, by creating new automatic systems also socially acceptable, since they will be perfectly coherent with the current laws on privacy.

This project aims at developing innovative solutions for detecting people in an automatic way by processing videos in real-time. Original and robust techniques will be applied to installations free from structural constraints, and, in particular, to multicamera distributed systems (with fixed, PTZ and mobile cameras) coordinated with sensors networks. The visual >>>

Principal Investigator
Rita Cucchiara Università degli Studi di MODENA e REGGIO EMILIA
Research Objectives
The FREE SURF project aims at proposing new technologies for the next generations of video surveillance systems oriented to the automatic real-time control of the presence and actions undertaken by people in the environment, without the direct control of a human operator.
The FREE SURF project is born with a twofold aim: first, innovative scientific research in the field of Computer Vision and Pattern Recognition, second, innovative applied research for the development of new generations of video surveillance systems, both effective and socially acceptable with respect to privacy concerns.

The first objective is to conduct a thoughtful research activity in the field of Computer Engineering for video surveillance of people in “structural constraint FREE” systems, that is in systems free from structural and environmental constraints.
The automatic visual control of human presence and actions in a given environment is, indeed, one of the most studied problems in the last decade. Nowadays, a very large literature exists, which presents algorithms and robust implementations for the recognition of single persons, in structured environments: closed environments with controlled illumination, open environments with large field of view (in order to consider people as small rigid moving objects), with few people, with only partially occluded fields of view, controlled by fixed cameras (to segment objects as different from the background), and installed with >>>

Timescale
24 months
National and international background
Real time video processing and automatic people detection, localization and tracking are intrinsically complex problems, with a lot of international challenges. Videosurveillance of people and human action control is one of the more discussed arguments in Computer Vision, Pattern Recognition and Multimedia. The first important special issue on this topic was published on IEEE Transaction on PAMI in 2000, in which the first video surveillance systems were presented; for example, it collected proposal like the background suppression algorithm of Stauffer and Grimson[39], that is a reference work in this field, the W4 system from Maryland [42], the PFinder system of Pentland [43].
The current interest on this field is proved by the several conferences presenting papers on this topic such as (CVPR, ICCV, ECCV, ICPR) or devoted to video surveillance like IEEE Int. Conf. on Advanced Video Surv. Systems, IEEE Workshop. on Video Surveillance, and ACM Workshop on Video Surveillance and Sensor Networks.
Intrinsically difficulties of people video surveillance are due to several factors such as shape changing of the human body, its non rigid motion, variable posture and gait, presence of occlusions and interaction between people simultaneously present in the scene, presence of infrastructures in the indoor and outdoor environment (e.g., doors and furniture in indoor scenes, vehicles, trees and urban furniture in outdoor scenes), illumination changes and shadows [51]. In >>>