Object Detection and Recognition for Assistive Robots: Experimentation and Implementation
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comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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Título
Object Detection and Recognition for Assistive Robots: Experimentation and ImplementationFecha de publicación
2017-09Editor
Institute of Electrical and Electronics Engineers (IEEE)Cita bibliográfica
MARTINEZ-MARTIN, Ester; DEL POBIL, Angel P. Object Detection and Recognition for Assistive Robots. IEEE Robotics & Automation Magazine, 2017.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://ieeexplore.ieee.org/abstract/document/7885021/Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Technological advances are being made to assist humans in performing ordinary tasks in everyday settings. A key issue is the interaction with objects of varying size, shape, and degree of mobility. Autonomous assistive ... [+]
Technological advances are being made to assist humans in performing ordinary tasks in everyday settings. A key issue is the interaction with objects of varying size, shape, and degree of mobility. Autonomous assistive robots must be provided with the ability to process visual data in real time so that they can react adequately for quickly adapting to changes in the environment. Reliable object detection and recognition is usually a necessary early step to achieve this goal. In spite of significant research achievements, this issue still remains a challenge when real-life scenarios are considered. In this article, we present a vision system for assistive robots that is able to detect and recognize objects from a visual input in ordinary environments in real time. The system computes color, motion, and shape cues, combining them in a probabilistic manner to accurately achieve object detection and recognition, taking some inspiration from vision science. In addition, with the purpose of processing the input visual data in real time, a graphical processing unit (GPU) has been employed. The presented approach has been implemented and evaluated on a humanoid robot torso located at realistic scenarios. For further experimental validation, a public image repository for object recognition has been used, allowing a quantitative comparison with respect to other state-of-the-art techniques when realworld scenes are considered. Finally, a temporal analysis of the performance is provided with respect to image resolution and the number of target objects in the scene. [-]
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