fall detection databases - 跌倒 / 摔倒检测数据集

fall detection databases - 跌倒 / 摔倒检测数据集

• the UR fall detection dataset (URFD)
• the fall detection dataset (FDD)

1. UR Fall Detection Dataset

http://fenix.univ.rzeszow.pl/~mkepski/ds/uf.html

Interdisciplinary Centre for Computational Modelling
University of Rzeszow - 热舒夫大学

interdisciplinary [ˌɪntədɪsəˈplɪnəri]：adj. 各学科间的

This dataset contains 70 (30 falls + 40 activities of daily living) sequences. Fall events are recorded with 2 Microsoft Kinect cameras and corresponding accelerometric data. ADL events are recorded with only one device (camera 0) and accelerometer. Sensor data was collected using PS Move (60Hz) and x-IMU (256Hz) devices.
该数据集包含 70 个 (30 个跌倒 + 40 个日常生活活动) 序列。使用 2 台 Microsoft Kinect 相机和相应的加速度计数据记录跌倒事件。ADL 事件仅用一台设备 (camera 0) 和加速度计记录。使用 PS Move (60Hz) 和 x-IMU (256Hz) 设备收集传感器数据。

The dataset is organized as follows. Each row contains sequence of depth and RGB images for camera 0 and camera 1 (parallel to the floor and ceiling mounted, respectively), synchronization data, and raw accelerometer data. Each video stream is stored in separate zip archive in form of png image sequence.
数据集组织如下。camera 0 and camera 1 每行包含深度和 RGB 图像序列 (平行于地板和吸顶分别安装)，同步数据和原始加速度计数据。每个视频流都以 png 图像序列的形式存储在单独的 zip 存档中。

2. Fall detection Dataset

http://falldataset.com/

The datasets that are used for the simulation purpose are raw RGB and Depth images of size 320x240 recorded from a single uncalibrated Kinect sensor after resizing from 640x480.

The Kinect sensor is fixed at roof height of approx 2.4m. The datasets contain a total of 21499 images. Out of total datasets of 22636 images, 16794 images can be used for training, 3299 images can be used for validation and 2543 images can be used for the test.
Kinect 传感器固定在屋顶高度约 2.4m 处。数据集总共包含 21499 张图像。在 22636 张图像的全部数据集中，可以使用 16794 张图像进行训练，可以使用 3299 张图像进行验证，可以使用 2543 张图像进行测试。

The images in the dataset are recorded in 5 different rooms which consist of 8 different view angles. There are 5 different participants out of which there are two male participants of age 32 and 50 and three female participants of age 19, 28 and 40. All the activities of the participants represent 5 different categories of poses that are standing, sitting, lying, bending and crawling. There is only one participant in each image. Some images in the datasets are empty which are categorised as other.
The images in the dataset are recorded in 5 different rooms which consist of 8 different view angles. 有 5 位不同的参与者，其中有两名 32 岁和 50 岁的男性参与者和三名 19、28 和 40 岁的女性参与者。参与者的所有活动都代表 5 种不同的姿势，这些姿势包括站立、坐着、躺着、弯曲和爬行。每个图像中只有一个参与者。数据集中的某些图像为空，被归类为 other。

We have used images of 2 participants: the male of age 32 and the female of age 28 combining total of 16794 images for training, and 3299 images for validation which contains a male participant of age 32 from training set but is in a different room to that of training and testing set. Similarly, the test set contains images of 3 participants out of which 2 female participants are of age 19 and 40 and a male participant is of age 50.
我们使用了 2 位参与者的图像：32 岁的男性和 28 岁的女性，总共 16794 张训练图像，3299 张验证图像，其中训练集包含来自 32 岁的男性参与者，但是位于不同的房间训练和测试集。同样，测试集包含 3 名参与者的图像，其中 2 名女性参与者的年龄分别为 19 岁和 40 岁，男性参与者的年龄为 50 岁。

These images are recorded in a different room that is not seen in training or validation set. These total of 22636 images are in sequence but have not repeated anywhere in the sequence and all the sets have original and its horizontal flipped images added in sequence to increase the number of images in a set.
These images are recorded in a different room that is not seen in training or validation set. 这些总共 22636 张图像是按顺序排列的，但是没有在序列中的任何位置重复，并且所有集合都依次添加了原始图像和其水平翻转图像，以增加集合中图像的数量。