Automated generation of synthetic in-car dataset for human body pose detection

In this paper, a toolchain for the generation of realistic synthetic images for human body pose detection in an in-car environment is proposed. The toolchain creates a customized synthetic environment, comprising human models, car, and camera. Poses are automatically generated for each human, taking into account a per-joint axis Gaussian distribution, constrained by anthropometric and range of motion measurements. Scene validation is done through collision detection. Rendering is focused on vision data, supporting time-of-flight (ToF) and RGB cameras, generating synthetic images from these sensors. Ground-truth data is then generated, comprising the car occupants' body pose (2D/3D), as well as full body RGB segmentation frames with different body parts' labels. We demonstrate the feasibility of using synthetic data, combined with real data, to train distinct machine learning agorithms, demonstrating the improvement in their algorithmic accuracy for the in-car scenario.This work is supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project no 039334; Funding Reference: POCI-01-0247-FEDER-039334]

AI based monitoring violent action detection data for in-vehicle scenarios

With the evolution of technology associated with mobility and autonomy, Shared Autonomous Vehicles will be a reality. To ensure passenger safety, there is a need to create a monitoring system inside the vehicle capable of recognizing human actions. We introduce two datasets to train human action recognition inside the vehicle, focusing on violence detection. The InCar dataset tackles violent actions for in-car background which give us more realistic data. The InVicon dataset although doesn't have the realistic background as the InCar dataset can provide skeleton (3D body joints) data. This datasets were recorded with RGB, Depth, Ther-mal, Event-based, and Skeleton data. The resulting dataset contains 6 400 video samples and more than 3 million frames, collected from sixteen distinct subjects. The dataset contains 58 action classes, including violent and neutral (i.e., non-violent) activities.(c) 2022 Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )This work has been supported by FCT-Fundacao para a Ciencia e Tecnologia within the R & D Units Project Scope: UIDB/00319/2020. This work was partly financed by European social funds through the Portugal 2020 program and by national funds through FCT-Foundation for Science and Technology within the scope of projects POCH-02-5369-FSE-000006. The author would also like to acknowledge FCT for the attributed Doctoral grant PD/BDE/150500/2019

MoLa R10k InCar Dataset

This repository presents the MoLa R10k InCar Dataset.The repository is divided in 2 parts (Pt#) and includes a real dataset. To ease its use, a general README file is provided, explaining the structure of an individual sub-dataset. For visualization purposes, a toolbox is provided in MATLAB code that reads the individual sub-dataset and generates an animation with image and ground-truth.Part 1 - http://dx.doi.org/10.17632/hj634mwk24.1Part 2 - http://dx.doi.org/10.17632/58db7xy5p3.1All publications using MoLa R10k InCar Dataset, should cite the following paper:João Borges; Bruno Oliveira; Helena Torres; Nelson Rodrigues; Sandro Queirós; Maximilian Shiller; Victor Coelho; Johannes Pallauf; José Henrique Brito; José Mendes; Jaime Cruz Fonseca (2020), “Automated generation of synthetic in-car dataset for human body pose detection”, Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISAPP 2020

MoLa R10k InCar Dataset Pt2

This repository presents the MoLa R10k InCar Dataset.The repository is divided in 2 parts (Pt#) and includes a real dataset. To ease its use, a general README file is provided, explaining the structure of an individual sub-dataset. For visualization purposes, a toolbox is provided in MATLAB code that reads the individual sub-dataset and generates an animation with image and ground-truth.Part 1 - http://dx.doi.org/10.17632/hj634mwk24.1Part 2 - http://dx.doi.org/10.17632/58db7xy5p3.1All publications using MoLa R10k InCar Dataset, should cite the following paper:João Borges; Bruno Oliveira; Helena Torres; Nelson Rodrigues; Sandro Queirós; Maximilian Shiller; Victor Coelho; Johannes Pallauf; José Henrique Brito; José Mendes; Jaime Cruz Fonseca (2020), “”Automated generation of synthetic in-car dataset for human body pose detection”, Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISAPP 2020

MoLa R10k InCar Dataset

This repository presents the MoLa R10k InCar Dataset.The repository is divided in 2 parts (Pt#) and includes a real dataset. To ease its use, a general README file is provided, explaining the structure of an individual sub-dataset. For visualization purposes, a toolbox is provided in MATLAB code that reads the individual sub-dataset and generates an animation with image and ground-truth.Part 1 - http://dx.doi.org/10.17632/hj634mwk24.1Part 2 - http://dx.doi.org/10.17632/58db7xy5p3.1All publications using MoLa R10k InCar Dataset, should cite the following paper:João Borges; Bruno Oliveira; Helena Torres; Nelson Rodrigues; Sandro Queirós; Maximilian Shiller; Victor Coelho; Johannes Pallauf; José Henrique Brito; José Mendes; Jaime Cruz Fonseca (2020), “Automated generation of synthetic in-car dataset for human body pose detection”, Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISAPP 2020.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

MoLa R10k InCar Dataset Pt2

This repository presents the MoLa R10k InCar Dataset.The repository is divided in 2 parts (Pt#) and includes a real dataset. To ease its use, a general README file is provided, explaining the structure of an individual sub-dataset. For visualization purposes, a toolbox is provided in MATLAB code that reads the individual sub-dataset and generates an animation with image and ground-truth.Part 1 - http://dx.doi.org/10.17632/hj634mwk24.1Part 2 - http://dx.doi.org/10.17632/58db7xy5p3.1All publications using MoLa R10k InCar Dataset, should cite the following paper:João Borges; Bruno Oliveira; Helena Torres; Nelson Rodrigues; Sandro Queirós; Maximilian Shiller; Victor Coelho; Johannes Pallauf; José Henrique Brito; José Mendes; Jaime Cruz Fonseca (2020), “”Automated generation of synthetic in-car dataset for human body pose detection”, Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISAPP 2020.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV