The dataset proposed is a collection of pedestrian navigation data sequences combining visual and spatial information. The pedestrian navigation sequences are situations encountered by a pedestrian walking in an urban outdoor environment, such as moving on the sidewalk, navigating through a crowd or crossing a street when the pedestrian light traffic is green. The acquired data is time-stamped and provide RGB-D images and associated with GPS, and inertial data (acceleration, rotation). These recordings were acquired by separate processes, avoiding delays during their capture in order to guarantee a synchronization between the moment of acquisition by the sensor and the moment of recording on the system. The acquisition was made in the city of Dijon, France, including narrow streets, wide avenues, and parks.
Annotations of the RGB-D are also provided by bounding boxes indicating the position of relevant static or dynamic object present in a pedestrian area such as a tree, bench, or person.
This pedestrian navigation dataset is proposed for the development of a mobility support system for visually impaired people in their daily movements in an outdoor environment. The visual data and localization sequences are used to elaborate the visual processing method to extract relevant information about the obstacle and the current position of the path. Alongside the dataset, a visual to auditory substitution method has been employed to convert each image sequence into corresponding stereophonic sound files, allowing for comparison and evaluation. Synthetics sequences associated with the same set of information are also provided based on the recordings of a displacement within the 3D model of a real place in Dijon.

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kc_raw_content:

The dataset proposed is a collection of pedestrian navigation data sequences combining visual and spatial information. The pedestrian navigation sequences are situations encountered by a pedestrian walking in an urban outdoor environment, such as moving on the sidewalk, navigating through a crowd or crossing a street when the pedestrian light traffic is green. The acquired data is time-stamped and provide RGB-D images and associated with GPS, and inertial data (acceleration, rotation). These recordings were acquired by separate processes, avoiding delays during their capture in order to guarantee a synchronization between the moment of acquisition by the sensor and the moment of recording on the system. The acquisition was made in the city of Dijon, France, including narrow streets, wide avenues, and parks.
Annotations of the RGB-D are also provided by bounding boxes indicating the position of relevant static or dynamic object present in a pedestrian area such as a tree, bench, or person.
This pedestrian navigation dataset is proposed for the development of a mobility support system for visually impaired people in their daily movements in an outdoor environment. The visual data and localization sequences are used to elaborate the visual processing method to extract relevant information about the obstacle and the current position of the path. Alongside the dataset, a visual to auditory substitution method has been employed to convert each image sequence into corresponding stereophonic sound files, allowing for comparison and evaluation. Synthetics sequences associated with the same set of information are also provided based on the recordings of a displacement within the 3D model of a real place in Dijon.

 

Access to data.

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