Toward Real-Time Extraction of Pedestrian
Contexts with Sterao Camera
Kei Suzuki 1 , Kazunori Takashio 2 , Hideyuki Tokuda 1,2 , Masaki Wada 3 , Yusuke Matsuki 3 , Kazunori Umeda 3
1 Graduate School of Media and Governance, Keio University,
2 Faculty of Environmental Information, Keio University and
3 Dept. Precision Mecanics, Faculty of Science and Engineering Chuo Univ. / CREST, JST
Email: email@example.com, firstname.lastname@example.org
Abstract—Our goal is to extract the mood of disquiet on
street corners in real-time with stereo video camera systems.
In the last year’s INSS, we proposed a novel stereo mesurement
algorithm to detect moving people, which was focusing on moving
region in video data. In this paper, we report our prototype of
probabilistic inference engine that can detect contexts of each
individual pedestrian and those of target pedestrian groups or
mobs. Here, we have tried to apply the Bayesian Network Model
to real-time context analysis.
Index Terms—distributed camera system, stereo vision, realtime
context analysis, pedestrian context, bayesian network
III. EXTRACTION PEDESTRIAN CONTEXTS FROM STERAO
A. Features of pedestrian contexts
(a) With motion detection(proposed method)
Fig. 1: Flow or our stereo algorithm
(b) With motion detection(proposed method)
Fig. 3: An example of our stereo algorithm: range images for
Fig. 2: An example scene
B. Bayesian Network Approach
Fig. 4: Thumble bayesian network model
Fig. 5: Friend group bayesian network model
C. Bayse model of Target pedestrian contexts
1) Context of individual pedestrian:
Thumble xs1, ys1,
z1 1 X , Y
, Z xs2, ys2, z2 xs1,
2) Context of group or mobs:
Group vs1, dist1, angle1
vs2, dist2, angle2
vs1, dist1, angle1 vs3,
dist3, angle3 vs2, dist2, angle2 1
IV. REAL-TIME EXTRACTION OF PEDESTRIAN CONTEXTS
(a) Experimental scene
Fig. 7: Prototype system
(b) Subtraction image
Fig. 6: Outdoor Example scene
Grey Research Bumblebee2f=3.8mm, XGA
E6850(2.6GHz 2 core)OSWindows Vista
The authors would like to thank...
 M. Bertozzi and E. Binelli and A. Broggi and M. Del Rose, Stereo Visionbased
approaches for Pedestrian Detection, CVPR ’05: Proceedings of
the 2005 IEEE Computer Society Conference on Computer Vision and
Pattern Recognition (CVPR’05) - Workshops, 2005.
 Z. Liang and C. Thorpe, Stereo- and Neural Network-Based Pedestrian
Detection, Proceedings of the IEEE Intelligent Transportation Systems
Conference, Tokyo, Japan, Spring, 1999.
 F. Xu and K. Fujimura, Pedestrian detection and tracking with night
vision, In IEEE Intelligent Vehicles Symposium, 2002. 212.
 R. E. Neapolitan. Learning Bayesian Networks. Prentice Hall, 2003.
 S. Russel and P. Norvig. Artificiall Intelligence Modern Approach Second
Edition. Prentice Hall, 2002.
 R. Fung, Chang, and K.-C. Weighting and integrating evidence for
stochasitic simulation in bayesian networks. In Fifth Conference on
Uncertainty in Artificial Intelligence, 1989.
 H. Guo and W. Hsu. A survey of algorithms for real-time bayesian
network inference, 2002.