© The Institution of Engineering and Technology
Many vision problems require fast and accurate tracking of objects in dynamic scenes. In this study, we propose an A* search algorithm through the space of transformations for computing fast target 2D motion. Two features are combined in order to compute efficient motion: (i) Kullback–Leibler measure as heuristic to guide the search process and (ii) incorporation of target dynamics into the search process for computing the most promising search alternatives. The result value of the quality of match computed by the A* search algorithm together with the more common views of the target object are used for verifying template updates. A template will be updated only when the target object has evolved to a transformed shape dissimilar with respect to the actual shape. The study includes experimental evaluations with video streams demonstrating the effectiveness and efficiency for real-time vision based tasks with rigid and deformable objects.
References
-
-
1)
-
G.D. Hager ,
P.N. Belhumeur
.
Efficient region tracking with parametric models of geometry and illumination.
IEEE Trans. Pattern Anal. Mach. Intell.
,
10 ,
1025 -
1039
-
2)
-
S. Baker ,
I. Matthews
.
Lucas–Kanade 20 years on: a unifying framework.
Int. J. Comput. Vis.
,
221 -
255
-
3)
-
R. Parra ,
M. Devy ,
M. Briot
.
(1999)
3D modelling and robot localization from visual and range data in natural scenes.
-
4)
-
S. Belongie ,
J. Malik ,
J. Puzicha
.
Shape matching and object recognition using shape contexts.
IEEE Trans. Pattern Anal. Mach. Intell.
,
4 ,
509 -
522
-
5)
-
S. Kullback
.
(1968)
Information theory and statistics.
-
6)
-
Adam, A., Rivlin, E., Shimshoni, I.: `Robust fragments-based tracking using the integral histogram', Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2006.
-
7)
-
E. Sánchez Nielsen ,
M. Hernández Tejera
.
Tracking moving objects using the Hausdorff distance. A method and experiments.
Front. Artif. Intell. Appl.: Pattern Recog. Appl.
,
164 -
172
-
8)
-
T.M. Cover ,
J.A. Thomas
.
(2006)
Elements of information theory.
-
9)
-
Reynolds, J.: `Autonomous underwater vehicle: vision system', 1998, PhD, Australian National University, Robotic systems Laboratory, Department of Engineering.
-
10)
-
C. Schlegel ,
J. Illmann ,
H. Jaberg ,
M. Schuster ,
R. Worz
.
(1999)
Integrating Vision based bejaviours with an autonomous robot.
-
11)
-
Y. Chen ,
G. Medioni
.
Object modelling by registration of multiple range images.
Image Vis. Comput.
,
3 ,
145 -
155
-
12)
-
I. Matthews ,
T. Ishikawa ,
S. Baker
.
The template update problem.
IEEE Trans. Pattern Anal. Mach. Intell.
,
6 ,
810 -
815
-
13)
-
J. Canny
.
A computational approach to edge detection.
IEEE Trans. Pattern Anal. Mach. Intell.
,
6 ,
679 -
697
-
14)
-
D.P. Huttenlocher ,
G.A. Klanderman ,
W.J. Rucklidge
.
Comparing images using the Hausdorff distance.
IEEE Trans. Pattern Anal. Mach. Intell.
,
9 ,
850 -
863
-
15)
-
Y. Bar-Shalom ,
X.-R Li
.
(1993)
Estimation and tracking: principles, techniques, and software.
-
16)
-
J. Pearl
.
(1984)
Heuristics. Intelligent search strategies for computer problem solving.
-
17)
-
T.-L. Liu ,
H.-T. Chen
.
Real-time tracking using trust-region methods.
IEEE Trans. Pattern Anal. Mach. Intell.
,
3 ,
397 -
401
-
18)
-
W.J. Rucklidge
.
(1996)
Efficient computation of the minimum Hausdorff distance for visual recognition.
-
19)
-
Caviar datasets: http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1/, accessed September 2010.
-
20)
-
Comaniciu, D., Ramesh, V., Meer, P.: `Real-time tracking of non-rigid objects using mean shift', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2000, p. 142–149, vol. II.
-
21)
-
P.J. Besl ,
N.D. McKay
.
A method for registration of 3D shapes.
IEEE Trans. Pattern Anal. Mach. Intell.
,
2 ,
239 -
256
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2010.0032
Related content
content/journals/10.1049/iet-cvi.2010.0032
pub_keyword,iet_inspecKeyword,pub_concept
6
6