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Unfalsified visual servoing for simultaneous object recognition and pose tracking

Jiang, Ping; Cheng, Yongqiang; Wang, Xiaonian; Feng, Zuren


Ping Jiang

Xiaonian Wang

Zuren Feng


In a complex environment, simultaneous object recognition and tracking has been one of the challenging topics in computer vision and robotics. Current approaches are usually fragile due to spurious feature matching and local convergence for pose determination. Once a failure happens, these approaches lack a mechanism to recover automatically. In this paper, data-driven unfalsified control is proposed for solving this problem in visual servoing. It recognizes a target through matching image features with a 3-D model and then tracks them through dynamic visual servoing. The features can be falsified or unfalsified by a supervisory mechanism according to their tracking performance. Supervisory visual servoing is repeated until a consensus between the model and the selected features is reached, so that model recognition and object tracking are accomplished. Experiments show the effectiveness and robustness of the proposed algorithm to deal with matching and tracking failures caused by various disturbances, such as fast motion, occlusions, and illumination variation.


Jiang, P., Cheng, Y., Wang, X., & Feng, Z. (2016). Unfalsified visual servoing for simultaneous object recognition and pose tracking. IEEE Transactions on Cybernetics, 46(12), 3032-3046.

Journal Article Type Article
Acceptance Date Oct 10, 2015
Publication Date 2016-12
Deposit Date Feb 20, 2017
Publicly Available Date Feb 20, 2017
Journal IEEE transactions on cybernetics
Print ISSN 2168-2267
Electronic ISSN 2168-2275
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 46
Issue 12
Pages 3032-3046
Keywords Visual servoing, Visual tracking, Object recognition, Supervisory control, Unfalsified adaptive control
Public URL
Publisher URL
Additional Information This is a description of an article published in IEEE transactions on cybernetics, 2016, v.46 issue 12.


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