Estimating Human Body Orientation using Skeletons and Extreme Gradient Boosting

Paiva, Pedro Victor Vieira de; Batista, Murillo Rehder; Ramos, Josué Jr. Guimarães

doi:10.1109/lars/sbr/wre51543.2020.9307079

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Similarly to ( Ahuja et al, 2019 ; de Paiva et al, 2020 ), we use the OpenPose software for this purpose. This is a real-time multi-person system that jointly detects human body key-points on single images ( Cao et al, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

“…Young children begin to carry out actions with others using joint visual attention at around 9 months old (Tomasello, 2014). However, communication gaze and body orientation are also critical for non-human animals (Davidson and Clayton, 2016). Terrestrial predators often send an "honest signal" (Bradbury and Vehrencamp, 1998) suggesting their subsequent course of action (Book and Freeberg, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Although there are a number of algorithms to determine the orientation of people using only one camera ( Moreno-Noguer, 2016 ; de Paiva et al, 2020 ; Wu et al, 2020 ), they have several limitations for use in the classroom. They are focused on pedestrian body orientation and other situations that are of interest to autonomous vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Detection of Gaze and Body Orientation in Elementary School Classrooms

Araya

Sossa-Rivera

2021

Front. Robot. AI

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Detecting the direction of the gaze and orientation of the body of both teacher and students is essential to estimate who is paying attention to whom. It also provides vital clues for understanding their unconscious, non-verbal behavior. These are called “honest signals” since they are unconscious subtle patterns in our interaction with other people that help reveal the focus of our attention. Inside the classroom, they provide important clues about teaching practices and students' responses to different conscious and unconscious teaching strategies. Scanning this non-verbal behavior in the classroom can provide important feedback to the teacher in order for them to improve their teaching practices. This type of analysis usually requires sophisticated eye-tracking equipment, motion sensors, or multiple cameras. However, for this to be a useful tool in the teacher's daily practice, an alternative must be found using only a smartphone. A smartphone is the only instrument that a teacher always has at their disposal and is nowadays considered truly ubiquitous. Our study looks at data from a group of first-grade classrooms. We show how video recordings on a teacher's smartphone can be used in order to estimate the direction of the teacher and students’ gaze, as well as their body orientation. Using the output from the OpenPose software, we run Machine Learning (ML) algorithms to train an estimator to recognize the direction of the students’ gaze and body orientation. We found that the level of accuracy achieved is comparable to that of human observers watching frames from the videos. The mean square errors (RMSE) of the predicted pitch and yaw angles for head and body directions are on average 11% lower than the RMSE between human annotators. However, our solution is much faster, avoids the tedium of doing it manually, and makes it possible to design solutions that give the teacher feedback as soon as they finish the class.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Detection of Gaze and Body Orientation in Elementary School Classrooms

Araya

Sossa-Rivera

2021

Front. Robot. AI

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GRAN: graph recurrent attention network for pedestrian orientation classification

Shan

Liu

et al. 2022

Complex Intell. Syst.

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In complex traffic scenes, accurate identification of pedestrian orientations can help drivers determine pedestrian trajectories and help reduce traffic accidents. However, there are still many challenges in pedestrian orientation recognition. First, due to the irregular appearance of pedestrians, it is difficult for general Convolutional Neural Networks (CNNs) to extract discriminative features. In addition, more features of body parts help to judge the orientation of pedestrians. For example, head, arms and legs. However, they are usually small and not conducive to feature extraction. Therefore, in this work, we use several discrete values to define the orientation of pedestrians, and propose a Gated Graph Neural Network (GGNN)-based Graph Recurrent Attention Network (GRAN) to classify the orientation of pedestrians. The contributions are as follows: (1) We construct a body parts graph consisting of head, arms and legs on the feature maps output by the CNN backbone. (2) Mining the dependencies between body parts on the graph via the proposed GRAN, and utilizing the encoder–decoder to propagate features among graph nodes. (3) In this process, we propose an adjacency matrix with attention edge weights to dynamically represent graph node relationships, and the edge weights are learned during network training. To evaluate the proposed method, we conduct experiments on three different benchmarks (PDC, PDRD, and Cityscapes) with 8, 3, and 4 orientations, respectively. Note that the orientation labels for PDRD and Cityscapes are annotated by our hand. The proposed method achieves 97%, 91% and 90% classification accuracy on the three data sets, respectively. The results are all higher than current state-of-the-art methods, which demonstrate the effectiveness of the proposed method.

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Neural Network and Extreme Gradient Boosting in Near Infrared Spectroscopy

Chia

Suarin

2022

2022 International Conference on Innovations and Development of Information Technologies and Robotics (IDITR)

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Estimating Human Body Orientation using Skeletons and Extreme Gradient Boosting

Cited by 5 publications

References 15 publications

Automatic Detection of Gaze and Body Orientation in Elementary School Classrooms

Automatic Detection of Gaze and Body Orientation in Elementary School Classrooms

GRAN: graph recurrent attention network for pedestrian orientation classification

Neural Network and Extreme Gradient Boosting in Near Infrared Spectroscopy

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