Overview

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. The images in the dataset are ecorded 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'. 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. 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.

Format

Posses and labels:

Standing: class 1, Sitting: class 2, Lying: class 3, Bending: class 4, Crawling: class 5, Empty: class 0

Label format (CSV):

Instruction

Citation

@INPROCEEDINGS{7986795,
    author={K. {Adhikari} and H. {Bouchachia} and H. {Nait-Charif}},
    booktitle={2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)},
    title={Activity recognition for indoor fall detection using convolutional neural network},
    year={2017},
    volume={},
    number={},
    pages={81-84},
    doi={10.23919/MVA.2017.7986795}
}

License

Custom