Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns

Rodríguez-Moreno, Itsaso; Martínez-Otzeta, José María; Goienetxea, Izaro; Rodriguez, Igor; Sierra, Basilio

doi:10.3390/s20082436

Cited by 16 publications

(13 citation statements)

References 43 publications

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“…Most authors consider it unnecessary to describe the computer hardware used [ 66 , 68 , 71 , 73 , 74 ]. However, in Table 2 , it can be observed that computer equipment with basic hardware has been used [ 63 ], as well as robots such as FACE [ 67 ], Robovie R3 [ 69 ], and Pepper [ 70 ].…”

Section: Discussion and Conclusionmentioning

confidence: 99%

“…Then, the HMM and ANN algorithms are combined to determine the body posture. Furthermore, Rodríguez et al [ 70 ] proposed an algorithm for the Pepper robot to recognize the actions (say hello, shake hands) that a person performed to improve human–robot interaction. Their proposal consists of implementing the Kinect sensor to extract the skeleton using the OpenPose and the HOG algorithms.…”

Section: Algorithms Used For the Bodymentioning

confidence: 99%

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Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

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Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

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Section: Discussion and Conclusionmentioning

confidence: 99%

Section: Algorithms Used For the Bodymentioning

confidence: 99%

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The method presented in this paper is a continuation of the work presented in [2], which uses CSP applied to skeleton information for video action recognition. In this work, an image is obtained for each video that summarizes the information of the video and that can be then classified using image classifiers.…”

Section: Proposed Approachmentioning

confidence: 99%

“…The approach presented herein continues with the work presented in [2], where Common Spatial Patterns (CSP), a method commonly used in Brain Computer Interface (BCI) for ElectroEncephaloGram (EEG) systems [3], [4], is used as feature extraction method for a video action recognition task.…”

Section: Introductionmentioning

confidence: 98%

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A New Approach for Video Action Recognition: CSP-Based Filtering for Video to Image Transformation

et al. 2021

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In this paper we report on the design of a pipeline involving Common Spatial Patterns (CSP), a signal processing approach commonly used in the field of electroencephalography (EEG), matrix representation of features and image classification to categorize videos taken by a humanoid robot. The ultimate goal is to endow the robot with action recognition capabilities for a more natural social interaction. Summarizing, we apply the CSP algorithm to a set of signals obtained for each video by extracting skeleton joints of the person performing the action. From the transformed signals a summary image is obtained for each video, and these images are then classified using two different approaches; global visual descriptors and convolutional neural networks. The presented approach has been tested on two data sets that represent two scenarios with common characteristics. The first one is a data set with 46 individuals performing 6 different actions. In order to create the group of signals of each video, OpenPose has been used to extract the skeleton joints of the person performing the actions. The second data set is an Argentinian Sign Language data set (LSA64) from which the signs performed using just the right hand have been used. In this case the joint signals have been obtained using MediaPipe. The results obtained with the presented method have been compared with a Long Short-Term Memory (LSTM) method, achieving promising results.

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Early, intermediate and late fusion strategies for robust deep learning-based multimodal action recognition

Boulahia

Amamra

Madi

et al. 2021

Machine Vision and Applications

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Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns

Cited by 16 publications

References 43 publications

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

A New Approach for Video Action Recognition: CSP-Based Filtering for Video to Image Transformation

Early, intermediate and late fusion strategies for robust deep learning-based multimodal action recognition

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