Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition

first_page settings Open AccessArticle Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition by Itsaso Rodríguez-Moreno * [OrcID] , José María Martínez-Otzeta [OrcID] , Basilio Sierra [OrcID] , Itziar Irigoien , Igor Rodriguez-Rodriguez and Izaro Goienetxea [OrcID] Department of Computer Science and Artificial Intelligence, University of the Basque Country, Manuel Lardizabal 1, 20018 Donostia-San Sebastián, Spain * Author to whom correspondence should be addressed. Appl. Sci. 2020, 10(22), 8075; https://doi.org/10.3390/app10228075 Received: 1 October 2020 / Revised: 30 October 2020 / Accepted: 11 November 2020 / Published: 14 November 2020 (This article belongs to the Special Issue Advanced Intelligent Imaging Technology Ⅱ) Download PDF Browse Figures Abstract Video activity recognition, despite being an emerging task, has been the subject of important research due to the importance of its everyday applications. Video camera surveillance could benefit greatly from advances in this field. In the area of robotics, the tasks of autonomous navigation or social interaction could also take advantage of the knowledge extracted from live video recording. In this paper, a new approach for video action recognition is presented. The new technique consists of introducing a method, which is usually used in Brain Computer Interface (BCI) for electroencephalography (EEG) systems, and adapting it to this problem. After describing the technique, achieved results are shown and a comparison with another method is carried out to analyze the performance of our new approach.This work has been partially funded by the Basque Government, Research Teams grant number IT900-16, ELKARTEK 3KIA project KK-2020/00049, and the Spanish Ministry of Science (MCIU), the State Research Agency (AEI), and the European Regional Development Fund (FEDER), grant number RTI2018-093337-B-I100 (MCIU/AEI/FEDER, UE). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research