MIUN dataset för semi-autonom manövrering av eldrivna rullstolar MIUN-Feet dataset MIUN dataset for semi-autonomous powered wheelchair control 2021-303-1-1 https://doi.org/10.5878/k44d-3y06 Swedish National Data Service Svensk nationell datatjänst Landing page MIUN dataset för semi-autonom manövrering av eldrivna rullstolar MIUN-Feet dataset MIUN dataset for semi-autonomous powered wheelchair control 2021-303-1-1 https://doi.org/10.5878/k44d-3y06 10.3390/jimaging7120255 urn:nbn:se:miun:diva-43838 Vilar, Cristian Vilar, Cristian Swedish National Data Service Svensk nationell datatjänst Landing page A powered wheelchair detects and follows a caregiver walking beside it. Caregiver recognition is performed by detecting the caregiver feet above the floor. The dataset contains a set of images and annotated labels for a feet recognition application. The camera is placed in the right armrest of the powered wheelchair, tilted down 45 degrees. The camera measures the caregiver's feet walking beside the powered wheelchair. The dataset also includes the training, validation and test files definition for different camera image outputs (Depth, RGB, RGB-D), scenarios and light conditions. Images are captured by an Intel Realsense D455 depth camera. The dataset includes separate image files for each camera output (Depth, RGB, RGBD) and annotated labels for each frame. Each camera measurement contains 30 seconds of images, recorded at 6 frames/second (180 frames). The total number of images is 6000 (3000 including feet labels and 3000 without). The labelling process has been performed using the software labelImg. Sample scenarios and related frame numbers are defined in the readme.txt file. En permobil utrustad med kamera för att detekterar en vårdgivarens fötter bredvid rullstolen. Datasetet innehåller bilder (både färgbilder och distansbilder) med och utan fötter samt annotering av varje objekt som ska detekteras. Bilderna är tagna med en kamera placerad i det högra armstödet av permobilen, lutad 45 grader nedåt. Kameran avbildar vårdgivarens fötter som går bredvid den eldrivna rullstolen. Datasetet innehåller tränings-, validerings- och testfiler med annotering för olika kamerakonfigurationer, (djupbilder, färgbilder (RGB) och kombinerade färg- och djupbilder). Bilderna är insamlade med olika scenarier och ljusförhållanden. Bilderna i datasetet har tagits med en Intel Realsense D455 djupkamera. Datasetet innehåller separata bildfiler för varje kamerautgång (djup, RGB, RGB+djup) och annoterade etiketter för varje bild. Varje sekvens innehåller 30 sekunders bilder, inspelade med 6 bilder/sekund (180 bilder). Det totala antalet bilder är 6000 (3000 bilder med fötter och 3000 utan). Annoteringen av bilderna har utförts med programvaran labelImg. Varje sekvensscenarie och relaterade ramnummer definieras i filen readme.txt. Text Still image The camera is placed in the right armrest of the powered wheelchair, tilted down 45 degrees. The camera measures the caregiver's feet walking beside the powered wheelchair. The dataset also includes the training, validation and test files definition for different camera image outputs (Depth, RGB, RGB-D), scenarios and light conditions.The camera is placed in the right armrest of the powered wheelchair, tilted down 45 degrees. The camera measures the caregiver's feet walking beside the powered wheelchair. The dataset also includes the training, validation and test files definition for different camera image outputs (Depth, RGB, RGB-D), scenarios and light conditions. Access to data through SND. Data are freely accessible. Åtkomst till data via SND. Data är fritt tillgängliga. openAccess Giménez, Cristian V., Silvia Krug, Faisal Z. Qureshi, and Mattias O’Nils. 2021. "Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation" Journal of Imaging 7, no. 12: 255. https://doi.org/10.3390/jimaging7120255 Giménez, Cristian V., Silvia Krug, Faisal Z. Qureshi, and Mattias O’Nils. 2021. "Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation" Journal of Imaging 7, no. 12: 255. https://doi.org/10.3390/jimaging7120255 10.3390/jimaging7120255 urn:nbn:se:miun:diva-43838 2021