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Http://imagelab.ing.unimo.it Tutorial: multicamera and distributed video surveillance Third ACM/IEEE International Conference on Distributed Smart Cameras
http://imagelab.ing.unimo.it Tutorial: multicamera and
distributed video surveillance Third ACM/IEEE International
Conference on Distributed Smart Cameras ICDSC 2009 30/08/2009 Como
(Italy) Prof. Rita Cucchiara Universit di Modena e Reggio Emilia,
Italy
Slide 2
Distributed surveillance Problem of tracking and distributed
consistent labeling : Problem of matching or recognizing objects
previously viewed by other cameras. Some constraints: Constraints
on the motion models and transition times Scene planarity for both
overlapping and not overlapping FOVs Constraints of recurrent paths
[70] V. Kettenker, R. Zabih Bayesian multi camera surveillance CVPR
1999 [54] C.Stauffer, K.Tieu automated multi- camera planar
tracking correspondence modeling cvpr 2003
Slide 3
Distributed surveillance (cont.) Network of (smart) cameras;
Not overlapped FoVs; loosely coupled. Problems of node
communication If moving cameras: problems of calibration and
tracking. The simultaneous localization and tracking ( SLAT )
problem, to estimate both the trajectory of the object and the
poses of the cameras. Problem of color calibration [71]Zoltan
Safar, John Aa. Srensen, Jianjun Chen, and Kare J. Kristoffersen
MULTIMODAL WIRELESS NETWORKS: DISTRIBUTED SURVEILLANCE WITH
MULTIPLE NODES Proc of ICASSP 2005 [72]Funiak, S.; Guestrin, C.;
Paskin, M.; Sukthankar, R.; Distributed localization of networked
cameras Int conf on Information Processing in Sensor Networks,
2006. Information Processing in Sensor Networks, 2006.
originalIndependent channels Look-up table Full matrix
Slide 4
Color calibration Methods: Linear transformation Independent
channels Full matrix ( M conmputed with LSQ) Look-up table for non
linear transformation [73]Roullot, E., "A unifying framework for
color image calibration," 15th International Conference on Systems,
Signals and Image Processing, 2008. IWSSIP 2008, pp.97-100, 25-28
June 2008 [74]K. Yamamoto and J. U Color Calibration for
Multi-Camera System by using Color Pattern Board Technical Report
MECSE-3-2006
Slide 5
Feature to match Color (single / multiple) Shape (geometrical
ratios / spline / elliptical models) Motion (speed, direction) Gait
(Fourier transform) SIFT +, grey level co-occurrence matrix,
Zernike moments and some simple colour features Polar color
histogram + Shape [75]Nicholas J. Redding, Julius Fabian Ohmer1,
Judd Kelly1 & Tristrom Cooke Cross-Matching via Feature
Matching for Camera Handover with Non-Overlapping Fields of View
Proc. Of DICTA2008 [76]Kang, Jinman; Cohen, Isaac; Medioni, Gerard,
"Persistent Objects Tracking Across Multiple Non Overlapping
Cameras," IEEE Workshop on Motion and Video Computing, 2005.
WACV/MOTIONS '05, vol.2, no., pp.112-119, Jan. 2005
Slide 6
Distributed Surveillance at ImageLab The problem: a people
disappeared in the scene exiting from a camera FoV, where can be
detected in the future? 1) tracking within a camera FoV multi
hypothesis generation 2) tracking in exit zones 3) Prediction into
new cameras FoVs 4) matching in the entering zones Using Particle
Filtering + Pathnodes In computer graphic all the possible avatar
positions are represented by nodes and the connecting arcs refers
to allowed paths. The sequence of visited nodes is called
pathnodes. A weight can be associated to each arc in order to give
some measures on it, such as the duration, the likelihood to be
chosen with respect to other paths, and so on. Weights can be
defined or learned in a testing phase [ 77]R. Vezzani, D. Baltieri,
R. Cucchiara, "Pathnodes integration of standalone Particle Filters
for people tracking on distributed surveillance systems" in
Proceedings of 25 ICIAP2009, 2009
Slide 7
Exploit the knowledge about the scene To avoid all-to-all
matches, the tracking system can exploit the knowledge about the
scene Preferential paths -> Pathnodes Border line / exit zones
Physical constraints & Forbidden zones NVR Temporal
constraints
Slide 8
Tracking with pathnode A possible path between Camera1 and
Camera 4
Slide 9
Pathnodes lead particle diffusion
Slide 10
Results with PF and pathnodes Single camera tracking:
Multicamera tracking Recall=90.27% Recall=84.16% Precision=88.64%
Precision=80.00%
Slide 11
Example Frame 431. a man #21 exits and his particles are
propagated Frame 452 a person # 22 exits too and also his particles
are propagated Frame 471 a people is detected in Camera #2 and the
particles of both # 21 and #22 are used but the ones of #22 match
and person 22 is recognized