Classification of humans social relations within urban areas
| Autor: | Castro Arcusa, Oscar, Repiso Polo, Ely, Garrell Zulueta, Anais, Sanfeliu Cortés, Alberto |
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| Přispěvatelé: | Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), European Commission, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. RAIG - Mobile Robotics and Artificial Intelligence Group, Universitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel·ligents |
| Rok vydání: | 2022 |
| Předmět: |
Robòtica
Informàtica::Automàtica i control [Àrees temàtiques de la UPC] Pedestrian group Human behavior classification Human behavior Conducta (Psicologia) Robotics Social relation Neural networks (Computer science) Interacció persona-robot Human–human accompaniment Xarxes neuronals (Informàtica) Human-human accompaniment Informàtica::Robòtica [Àrees temàtiques de la UPC] Human-robot interaction Pedestrian groups |
| Zdroj: | ROBOT2022: Fifth Iberian Robotics Conference ISBN: 9783031210648 |
| ISSN: | 2367-3370 |
| Popis: | Trabajo presentado en el ROBOT2022: Fifth Iberian Robotics Conference, celebrada en Zaragoza (España), del 20 al 22 de noviembre de 2022 This paper presents the design of deep learning architectures which allow to classify the social relationship existing between two people who are walking in a side-by-side formation into four possible categories --colleagues, couple, family or friendship. The models are developed using Neural Networks or Recurrent Neural Networks to achieve the classification and are trained and evaluated using a database obtained from humans walking together in an urban environment. The best achieved model accomplishes a good accuracy in the classification problem and its results enhance the outcomes from a previous study [1]. In addition, we have developed several models to classify the social interactions in two categories --¿intimate" and "acquaintances", where the best model achieves a very good performance, and for a real robot this classification is enough to be able to customize its behavior to its users. Furthermore, the proposed models show their future potential to improve its efficiency and to be implemented in a real robot. This work has been supported by the Artificial Intelligence for Human–Robot Interaction (AI4HRI) project ANR-20-IADJ-0006. Also, this Work has been supported under the Spanish State Research Agency through the ROCOTRANSP project (PID2019-106702RB-C21/AEI/10.13039/501100011033)) and the EU project CANOPIES (H2020- ICT-2020-2-101016906). |
| Databáze: | OpenAIRE |
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