TY - GEN
T1 - Repetitive motion detection for human behavior understanding from video images
AU - Kumdee, Orrawan
AU - Ritthipravat, Panrasee
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/28
Y1 - 2016/1/28
N2 - This paper aims to develop a technique for repetitive motion detection which is necessary for human behavior analysis particularly in children with autism spectrum disorders. Images from video sequences are mainly investigated. The technique uses image self-similarity measure, which is less sensitive to view changes, noise, and stable to low resolution images, as input data to multilayer perceptron neural network. Outputs of the network are composed of two classes, which are repetitive and non-repetitive motions. The classifier uses training data from a single person. The model is created by 10 fold cross validation. Trained network is tested with different data sets from seven normal subjects. The classification results show that the proposed technique provides an average accuracy of 0.9115 and can be used in real-time manner. In addition, the trained classifier is robust to images taken from different view.
AB - This paper aims to develop a technique for repetitive motion detection which is necessary for human behavior analysis particularly in children with autism spectrum disorders. Images from video sequences are mainly investigated. The technique uses image self-similarity measure, which is less sensitive to view changes, noise, and stable to low resolution images, as input data to multilayer perceptron neural network. Outputs of the network are composed of two classes, which are repetitive and non-repetitive motions. The classifier uses training data from a single person. The model is created by 10 fold cross validation. Trained network is tested with different data sets from seven normal subjects. The classification results show that the proposed technique provides an average accuracy of 0.9115 and can be used in real-time manner. In addition, the trained classifier is robust to images taken from different view.
KW - children with autistic spectrum disorder
KW - image self similarity measure
KW - neural networks
UR - https://www.scopus.com/pages/publications/84963829101
U2 - 10.1109/ISSPIT.2015.7394384
DO - 10.1109/ISSPIT.2015.7394384
M3 - Conference contribution
AN - SCOPUS:84963829101
T3 - 2015 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2015
SP - 484
EP - 489
BT - 2015 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2015
Y2 - 7 December 2015 through 10 December 2015
ER -