A Visual Attention Model for Robot Object Tracking

Jin-Kui Chu; Rong-Hua Li; Qing-Ying Li; Hong-Qing Wang

doi:10.1007/s11633-010-0039-1

February 2010

Article Contents

Jin-Kui Chu, Rong-Hua Li, Qing-Ying Li and Hong-Qing Wang. A Visual Attention Model for Robot Object Tracking. International Journal of Automation and Computing, vol. 7, no. 1, pp. 39-46, 2010. doi: 10.1007/s11633-010-0039-1

Cite as: Jin-Kui Chu, Rong-Hua Li, Qing-Ying Li and Hong-Qing Wang. A Visual Attention Model for Robot Object Tracking. International Journal of Automation and Computing, vol. 7, no. 1, pp. 39-46, 2010. doi: 10.1007/s11633-010-0039-1

A Visual Attention Model for Robot Object Tracking

1.
School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, PRC

Author Biography:

Corresponding author: Jin-Kui Chu graduated from Hangzhou Dianzi University(HDU),PRC in 1986.

Received: 2009-03-01

Fund Project:

supported by National Basic Research Program of China (973 Program)(No.2006CB300407);National Natural Science Foundation of China (No.50775017)

Abstract

Inspired by human behaviors, a robot object tracking model is proposed on the basis of visual attention mechanism, which is fit for the theory of topological perception. The model integrates the image-driven, bottom-up attention and the object-driven, top-down attention, whereas the previous attention model has mostly focused on either the bottom-up or top-down attention. By the bottom-up component, the whole scene is segmented into the ground region and the salient regions. Guided by top-down strategy which is achieved by a topological graph, the object regions are separated from the salient regions. The salient regions except the object regions are the barrier regions. In order to estimate the model, a mobile robot platform is developed, on which some experiments are implemented. The experimental results indicate that processing an image with a resolution of 752 480 pixels takes less than 200 ms and the object regions are unabridged. The analysis obtained by comparing the proposed model with the existing model demonstrates that the proposed model has some advantages in robot object tracking in terms of speed and efficiency.
- Object tracking,
- visual attention,
- topological perception,
- salient regions,
- weighted similarity equation

References

[1]	A.De Cabrol,T.Garcia,P.Bonnin,M.Chetto.A concept of dynamically reconfigurable real-time vision system for autonomous mobile robotics.International Journal of Au-tomation and Computing,vol.5,no.2,pp.174-184,2008.
[2]	J.D.Liu,H.Hu.Biologically inspired behaviour design for autonomous robotic fish.International Journal of Automa-tion and Computing,vol.3,no.4,pp.336-347,2006.
[3]	G.V.Lauder,E.J.Anderson,J.Tangorra,P.G.A.Mad-den.Fish biorobotics:Kinematics and hydrodynamics of self-propulsion.Journal of Experimental Biology,vol.210, no.16,pp.2767-2780,2007.
[4]	T.J.Hu,L.C.Shen,L.X.Lin,H.J.Xu.Biological inspira-tions,kinematics modeling,mechanism design and experi-ments on an undulating robotic fin inspired by Gymnarchus niloticus.Mechanism and Machine Theory,vol.44,no.3, pp.633-645,2009.
[5]	L.Itti,C.Koch.A comparison of feature combination strategies for saliency-based visual attention systems.In Proceedings of SPIE Human Vision and Electronic Imaging IV,San Jose,California,USA,pp.473-482,1999.
[6]	L.Itti.Models of Bottom-up and Top-down Visual Atten-tion,Ph.D.dissertation,California Institute of Technology, USA,2000.
[7]	L.Itti,C.Koch.Feature combination strategies for saliency-based visual attention systems.Journal of Elec-tronic Imaging,vol.10,no.1,pp.161-169,2001.
[8]	L.Itti,C.Koch,E.Niebur.A model of saliency-based vi-sual attention for rapid scene analysis.IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.20,no. 11,pp.1254-1259,1998.
[9]	D.Walther,U.Rutishauser,C.Koch,P.Perona.Selective visual attention enables learning and recognition of multi-ple objects in cluttered scenes.Computer Vision and Image Understanding,vol.100,no.1-2,pp.41-63,2005.
[10]	P.Zhang,R.S.Wang.Hierarchical data competition in the bottom-up visual attention system.Journal of Computer-aided Design and Computer Graphics.vol.17,no.8, pp.1667-1672,2005.(in Chinese)
[11]	M.Z.Aziz,B.Mertsching,M.Salah,E.N.Shafik,R.Stem-mer.Evaluation of visual attention models for robots.In Proceedings of the 4th IEEE International Conference on Computer Vision Systems,IEEE,Manhattan,USA,pp.20-25,2006.
[12]	L.Chen,S.W.Zhang,M.V.Srinivasan.Global perception in small brain:Topological pattern recognition in honey-bees.In Proceedings of the National Academy of Sciences of the United States of America,vol.100,no.11,pp.6884-6889,2003.
[13]	I.C.B.Goodhew,B.D.Hutt,K.Warwick.Control and experimentation of a personal robot tracking system.Inter-national Journal of Modelling,Identification and Control, vol.1,no.1,pp.4-12,2006.
[14]	P.Yang,W.Y.Wu,M.Moniri,C.C.Chibelushi.A sensor-based SLAM algorithm for camera tracking in virtual stu-dio.International Journal of Automation and Computing, vol.5,no.2,pp.152-162,2008.
[15]	D.Xu,M.Tan,X.G.Zhao,Z.G.Tu.Seam tracking and visual control for robotic arc welding based on structured light stereovision.International Journal of Automation and Computing,vol.1,no.1,pp.63-82,2004.
[16]	S.Wang,H.Z.Ai,K.Z.He.Di?erence image based multi-ple motion targets detection and tracking.Journal of Image and Graphics,vol.4(A),no.6,pp.470-475,1999.(in Chi-nese)
[17]	S.J.Sun,D.Haynor,Y.M.Kim.Motion estimation based on optical flow with adaptive gradients.In Proceedings of the International Conference on Image Processing,IEEE, Vancouver,BC,Canada,pp.852-855,2000.
[18]	P.J.Burt,E.H.Adelson.The Laplacian pyramid as a com-pact image code.IEEE Transactions on Communications, vol.31,no.4,pp.532-540,1983.
[19]	B.M.H.Romeny.Front-end Vision and Multiscale Image Analysis,Netherlands:Kluwer Academic Publisher,2003.
[20]	K.Fukunaga,L.D.Hostetler.The estimation of the gradi-ent of a density function,with applications in pattern recog-nition.IEEE Transactions on Information Theory,vol.21, no.1,pp.32-40,1975.
[21]	D.Comaniciu,P.Meer.Mean shift:A robust approach to-ward feature space analysis.IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.24,no.5,pp.603-619,2002.
[22]	R.H.Li,J.K.Chu,C.L.Xu,Q.Y.Li.Design and real-ization of intelligent mobile robot platform for micro navi-gation sensors.Transducer and Micro System Technologies, vol.27,no.10,pp.118-120,2008.(in Chinese)
[23]	C.Zhao,X.H.Shang,S.Q.Geng.Control system for mi-cro soccer robot.Journal of Harbin Institute of Technology, vol.35,no.9,pp.1033-1035,2003.(in Chinese)
[24]	D.Walther,U.Rutishauser,C.Koch,P.Perona.On the usefulness of attention for object recognition.In Proceed-ings of Workshop on Attention and Performance in Compu-tational Vision at Europeon Conference on Computer Vi-sion,Czech Republic,pp.96-103,2004.
[25]	D.Walther,L.Itti,M.Riesenhuber,T.Poggio,C.Koch. Attentional selection for object recognition-a gentle way. Lecture Notes in Computer Science,Springer,vol.2525,pp. 251-267,2002.

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1.
沈阳化工大学材料科学与工程学院沈阳 110142

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A Visual Attention Model for Robot Object Tracking

1. School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, PRC

Corresponding author: Jin-Kui Chu graduated from Hangzhou Dianzi University(HDU),PRC in 1986.

Received: 2009-03-01

Fund Project:

supported by National Basic Research Program of China (973 Program)(No.2006CB300407);National Natural Science Foundation of China (No.50775017)

Key words:

Abstract: Inspired by human behaviors, a robot object tracking model is proposed on the basis of visual attention mechanism, which is fit for the theory of topological perception. The model integrates the image-driven, bottom-up attention and the object-driven, top-down attention, whereas the previous attention model has mostly focused on either the bottom-up or top-down attention. By the bottom-up component, the whole scene is segmented into the ground region and the salient regions. Guided by top-down strategy which is achieved by a topological graph, the object regions are separated from the salient regions. The salient regions except the object regions are the barrier regions. In order to estimate the model, a mobile robot platform is developed, on which some experiments are implemented. The experimental results indicate that processing an image with a resolution of 752 480 pixels takes less than 200 ms and the object regions are unabridged. The analysis obtained by comparing the proposed model with the existing model demonstrates that the proposed model has some advantages in robot object tracking in terms of speed and efficiency.

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Reference (25)

[1]	A.De Cabrol,T.Garcia,P.Bonnin,M.Chetto.A concept of dynamically reconfigurable real-time vision system for autonomous mobile robotics.International Journal of Au-tomation and Computing,vol.5,no.2,pp.174-184,2008.
[2]	J.D.Liu,H.Hu.Biologically inspired behaviour design for autonomous robotic fish.International Journal of Automa-tion and Computing,vol.3,no.4,pp.336-347,2006.
[3]	G.V.Lauder,E.J.Anderson,J.Tangorra,P.G.A.Mad-den.Fish biorobotics:Kinematics and hydrodynamics of self-propulsion.Journal of Experimental Biology,vol.210, no.16,pp.2767-2780,2007.
[4]	T.J.Hu,L.C.Shen,L.X.Lin,H.J.Xu.Biological inspira-tions,kinematics modeling,mechanism design and experi-ments on an undulating robotic fin inspired by Gymnarchus niloticus.Mechanism and Machine Theory,vol.44,no.3, pp.633-645,2009.
[5]	L.Itti,C.Koch.A comparison of feature combination strategies for saliency-based visual attention systems.In Proceedings of SPIE Human Vision and Electronic Imaging IV,San Jose,California,USA,pp.473-482,1999.
[6]	L.Itti.Models of Bottom-up and Top-down Visual Atten-tion,Ph.D.dissertation,California Institute of Technology, USA,2000.
[7]	L.Itti,C.Koch.Feature combination strategies for saliency-based visual attention systems.Journal of Elec-tronic Imaging,vol.10,no.1,pp.161-169,2001.
[8]	L.Itti,C.Koch,E.Niebur.A model of saliency-based vi-sual attention for rapid scene analysis.IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.20,no. 11,pp.1254-1259,1998.
[9]	D.Walther,U.Rutishauser,C.Koch,P.Perona.Selective visual attention enables learning and recognition of multi-ple objects in cluttered scenes.Computer Vision and Image Understanding,vol.100,no.1-2,pp.41-63,2005.
[10]	P.Zhang,R.S.Wang.Hierarchical data competition in the bottom-up visual attention system.Journal of Computer-aided Design and Computer Graphics.vol.17,no.8, pp.1667-1672,2005.(in Chinese)
[11]	M.Z.Aziz,B.Mertsching,M.Salah,E.N.Shafik,R.Stem-mer.Evaluation of visual attention models for robots.In Proceedings of the 4th IEEE International Conference on Computer Vision Systems,IEEE,Manhattan,USA,pp.20-25,2006.
[12]	L.Chen,S.W.Zhang,M.V.Srinivasan.Global perception in small brain:Topological pattern recognition in honey-bees.In Proceedings of the National Academy of Sciences of the United States of America,vol.100,no.11,pp.6884-6889,2003.
[13]	I.C.B.Goodhew,B.D.Hutt,K.Warwick.Control and experimentation of a personal robot tracking system.Inter-national Journal of Modelling,Identification and Control, vol.1,no.1,pp.4-12,2006.
[14]	P.Yang,W.Y.Wu,M.Moniri,C.C.Chibelushi.A sensor-based SLAM algorithm for camera tracking in virtual stu-dio.International Journal of Automation and Computing, vol.5,no.2,pp.152-162,2008.
[15]	D.Xu,M.Tan,X.G.Zhao,Z.G.Tu.Seam tracking and visual control for robotic arc welding based on structured light stereovision.International Journal of Automation and Computing,vol.1,no.1,pp.63-82,2004.
[16]	S.Wang,H.Z.Ai,K.Z.He.Di?erence image based multi-ple motion targets detection and tracking.Journal of Image and Graphics,vol.4(A),no.6,pp.470-475,1999.(in Chi-nese)
[17]	S.J.Sun,D.Haynor,Y.M.Kim.Motion estimation based on optical flow with adaptive gradients.In Proceedings of the International Conference on Image Processing,IEEE, Vancouver,BC,Canada,pp.852-855,2000.
[18]	P.J.Burt,E.H.Adelson.The Laplacian pyramid as a com-pact image code.IEEE Transactions on Communications, vol.31,no.4,pp.532-540,1983.
[19]	B.M.H.Romeny.Front-end Vision and Multiscale Image Analysis,Netherlands:Kluwer Academic Publisher,2003.
[20]	K.Fukunaga,L.D.Hostetler.The estimation of the gradi-ent of a density function,with applications in pattern recog-nition.IEEE Transactions on Information Theory,vol.21, no.1,pp.32-40,1975.
[21]	D.Comaniciu,P.Meer.Mean shift:A robust approach to-ward feature space analysis.IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.24,no.5,pp.603-619,2002.
[22]	R.H.Li,J.K.Chu,C.L.Xu,Q.Y.Li.Design and real-ization of intelligent mobile robot platform for micro navi-gation sensors.Transducer and Micro System Technologies, vol.27,no.10,pp.118-120,2008.(in Chinese)
[23]	C.Zhao,X.H.Shang,S.Q.Geng.Control system for mi-cro soccer robot.Journal of Harbin Institute of Technology, vol.35,no.9,pp.1033-1035,2003.(in Chinese)
[24]	D.Walther,U.Rutishauser,C.Koch,P.Perona.On the usefulness of attention for object recognition.In Proceed-ings of Workshop on Attention and Performance in Compu-tational Vision at Europeon Conference on Computer Vi-sion,Czech Republic,pp.96-103,2004.
[25]	D.Walther,L.Itti,M.Riesenhuber,T.Poggio,C.Koch. Attentional selection for object recognition-a gentle way. Lecture Notes in Computer Science,Springer,vol.2525,pp. 251-267,2002.

A Visual Attention Model for Robot Object Tracking

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通讯作者: 陈斌, bchen63@163.com

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