Perception for detection and grasping

The final publication is available at link.springer.comThis research presents a methodology for the detection of the crawler used in the project AEROARMS. The approach consisted on using a two-step progressive strategy, going from rough detection and tracking, for approximation maneuvers, to an accurate positioning step based on fiducial markers. Two different methods are explained for the first step, one using efficient image segmentation approach; and the second one using Deep Learning techniques to detect the center of the crawler. The fiducial markers are used for precise localization of the crawler in a similar way as explained in earlier chapters. The methods can run in real-time.Peer ReviewedPostprint (author's final draft

Evaluation of on-line quality estimators for object tracking

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. C. SanMiguel, A. Cavallaro, and J. M. Martínez, "Evaluation of on-line quality estimators for object tracking", in 17th IEEE International Conference on Image Processing, ICIP 2010, p. 825-828Failure of tracking algorithms is inevitable in real and on-line tracking systems. The online estimation of the track quality is therefore desirable for detecting tracking failures while the algorithm is operating. In this paper, we propose a taxonomy and present a comparative evaluation of online quality estimators for video object tracking. The measures are compared over a heterogeneous video dataset with standard sequences. Among other results, the experiments show, that the Observation Likelihood (OL) measure is an appropriate quality measure for overall tracking performance evaluation, while the Template Inverse Matching (TIM) measure is appropriate to detect the start and the end instants of tracking failures.Work partially supported by the Spanish Government (TEC2007- 65400 SemanticVideo), Cátedra Infoglobal-UAM for “Nuevas Tecnologías de video aplicadas a la seguridad”, Consejería de Educación of the Comunidad de Madrid and European Social Fund. Part of the work reported in this paper was done during a research stay of the first author under a research grant (funded by UAM) at Queen Mary University of London (UK)