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oai:riuma.uma.es:10630/148812026-02-03T11:23:40Zcom_10630_2254col_10630_37953

Human and Object Recognition with a High-resolution tactile sensor Gómez-de-Gabriel, Jesús Manuel García-Cerezo, Alfonso José Gandarias, Juan Manuel Sensores This paper 1 describes the use of two artificial intelligence methods for object recognition via pressure images from a high-resolution tactile sensor. Both meth- ods follow the same procedure of feature extraction and posterior classification based on a supervised Supported Vector Machine (SVM). The two approaches differ on how features are extracted: while the first one uses the Speeded-Up Robust Features (SURF) descriptor, the other one employs a pre-trained Deep Convolutional Neural Network (DCNN). Besides, this work shows its applica- tion to object recognition for rescue robotics, by distinguishing between differ- ent body parts and inert objects. The performance analysis of the proposed methods is carried out with an experiment with 5-class non-human and 3-class human classification, providing a comparison in terms of accuracy and compu-tational load. Finally, it is discussed how feature-extraction based on SURF can be obtained up to five times faster compared to DCNN. On the other hand, the accuracy achieved using DCNN-based feature extraction can be 11.67% superior to SURF. 2017-12-13T08:56:49Z 2017-12-13T08:56:49Z 2017-10-29 journal article https://hdl.handle.net/10630/14881 eng IEEE Sensors 2017 Glasgow, Reino Unido 29/10/2017 http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional IEEE