Recognition of Confusing Objects for NAO Robot
Visual processing is one of the most essential tasks in robotics systems. However, it may be affected by many unfavourable factors in the operating environment which lead to imprecisions and uncertainties. Under those circumstances, we propose a multi-cam
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Abstract. Visual processing is one of the most essential tasks in robotics systems. However, it may be affected by many unfavourable factors in the operating environment which lead to imprecisions and uncertainties. Under those circumstances, we propose a multi-camera fusing method applied in a scenario of object recognition for a NAO robot. The cameras capture the same scenes at the same time, then extract feature points from the scene and give their belief about the classes of the detected objects. Dempster’s rule of combination is then used to fuse information from the cameras and provide a better decision. In order to take advantages of heterogeneous sensors fusion, we combine information from 2D and 3D cameras. The results of experiment prove the efficiency of the proposed approach.
Keywords: Object recognition theory · Camera fusion
1
· NAO robot · Uncertainty · Evidence
Introduction
With the very fast development of high technologies, robotics is now more and more important to human life. Specifically, vision processing is one of the most focused areas, which helps a robot increase its ability to learn in explored environments. This work considers a scenario in which a NAO robot can recognize previously learned objects by fusing multi-camera to increase the quality of recognition and reduce uncertainties and imprecisions. We first have a look at how the other works have dealt with object recognition, then propose a solution for the considered case. In fact, the problem of recognizing an object has been addressed for several decades. The number of methodologies is huge up to now; each of them tried to prove their strengths and overcame the weaknesses of the preceding solutions. For instances, Berg et al. [1] used Geometric Blur approach for feature descriptors and proposed an algorithm to calculate the correspondences between images. The query image was then classified according to its lowest cost of correspondence to the sample images. Besides that, Ling and Jacobs [2] introduced the term “inner-distance”as the length of the shortest path between c Springer International Publishing Switzerland 2016 J.P. Carvalho et al. (Eds.): IPMU 2016, Part I, CCIS 610, pp. 262–273, 2016. DOI: 10.1007/978-3-319-40596-4 23
Recognition of Confusing Objects for NAO Robot
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landmark points within the shape silhouette. The inner-distance was used to build shape representations and they helped to obtain good matching results. For some texture-based approaches, [3] proposed a texture descriptor based on Random Sets and experimentally showed that it outperformed the co-occurrence matrix descriptor. Decision tree induction was used in that work to learn the classifier. Another example can be found in [4] where color and texture information were both used in an agricultural scenario to recognize fruits. On the other hand, some context-based methods like [5–7] considered contextual information surrounding the target objects. These information come from the interaction among objects in the scene and they help to disambiguate app
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