Fall Detection Dataset (2014)

Data creators : Julien Dubois [1], Johel Miteran [1]
[1] : Imagerie et Vision Artificielle (UR 7535) (Université de Bourgogne)
Description :
In order to evaluate automatic fall detection methods, we build a dataset in realistic videosurveillance setting using a single camera. The frame rate is 25 frames/s and the resolution is 320×240 pixels. The video data illustrates the main difficulties of realistic video sequences that we can find at an elderly home environment, as well as in a simplest office room. Our video sequences contain variable illumination, and typical difficulties like occlusions or cluttered and textured background. The actors performed various normal daily activities and falls. The dataset contains 191 videos that we annotated, for evaluation purpose, with extra information representing the ground-truth of the fall position in the image sequence. Then, each frame of each video is annotated : the localization of the body is manually defined using bounding boxes. This annotation allows to evaluate the classification features independently from the automatic body detection.
Discipline :

General metadata

Data acquisition date : from 1 Sep 2013 ongoing
Data acquisition methods :
  • Experimental data :
    Generally, the few available datasets dedicated to fall detection use the same location for testing and training. Therefore, it does not enable to evaluate the robustness of the method to the location change between traning and testing. In order to evaluate this robustness, we recorded the video of our dataset from different locations, allowing to define several evaluation protocols (« Home », « Coffee room », « Office » and « Lecture room »).
Language : English (eng)
Formats : image/jpeg
Audience : Research
Publications :
Additional information :
Data collected as part of the PhD thesis of Imen Charfi "Automatic human fall detection based on spatio-temporal descriptors : definition of the method, evaluation of the performance and real-time implementation", co-supervised by Johel Miteran (ImViA, university of Burgundy), Rached Tourki (university of Monastir), Julien Dubois (ImViA, university of Burgundy).


1 file


Published : 09/04/2024 11:42 Size : 8.95 GB

Description : In order to evaluate our automatic fall detection method, we build a dataset in realistic videosurveillance setting using a single camera.

Record created 9 Apr 2024 by Cyrille Migniot.
Local identifier: FR-13002091000019-2024-04-09.


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Université de Bourgogne, Université de Franche-Comté, UTBM, AgroSup Dijon, ENSMM, BSB, Arts des Metiers