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Open AccessArticle

Lightweight Active Object Retrieval with Weak Classifiers

1
Department of Electrical Engineering and Information Systems, University of Pannonia, Veszprém 8200, Hungary
2
Department of Information System, Faculty of Computers and Informatics, Benha University, Benha 13518, Egypt
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(3), 801; https://doi.org/10.3390/s18030801
Received: 19 January 2018 / Revised: 4 March 2018 / Accepted: 5 March 2018 / Published: 7 March 2018
(This article belongs to the Special Issue Sensor Signal and Information Processing)
In the last few years, there has been a steadily growing interest in autonomous vehicles and robotic systems. While many of these agents are expected to have limited resources, these systems should be able to dynamically interact with other objects in their environment. We present an approach where lightweight sensory and processing techniques, requiring very limited memory and processing power, can be successfully applied to the task of object retrieval using sensors of different modalities. We use the Hough framework to fuse optical and orientation information of the different views of the objects. In the presented spatio-temporal perception technique, we apply active vision, where, based on the analysis of initial measurements, the direction of the next view is determined to increase the hit-rate of retrieval. The performance of the proposed methods is shown on three datasets loaded with heavy noise. View Full-Text
Keywords: object retrieval; Hough transformation; sensor fusion; active vision object retrieval; Hough transformation; sensor fusion; active vision
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Czúni, L.; Rashad, M. Lightweight Active Object Retrieval with Weak Classifiers. Sensors 2018, 18, 801.

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