Title :
Human Gait Recognition and Classification Using Time Series Shapelets
Author :
Shajina, T. ; Sivakumar, P. Bagavathi
Author_Institution :
Amrita Sch. of Eng., Amrita Vishwa Vidyapeetham, Coimbatore, India
Abstract :
Human gait is the main activity of daily life. Gait can be used for applications like human identification (in medical field etc). Since gait can be perceived from a distance it can be used for human identification. Gait recognition means identifying the person with his/her gait. Human identification using gait can be used in surveillance. A method is proposed for gait recognition using a technique which uses time series shapelets. First, for a gait video a preprocessing is done to extract the silhouette images from the video. From these silhouette images features like joint angle and swing distance are extracted which can be represented as the time series data. From this time series data, time series shapelets are extracted. Shapelets are subsequence of time series data which can discriminate between classes. Shapelets are maximally representative of the class. These time series shapelets can be used to identify human by their gait. Shapelets can also be used for classification. After extracting the shapelets, the prediction is done using the decision tree. In that it can be used for classifying normal and abnormal human gait.
Keywords :
decision trees; feature extraction; gait analysis; image classification; image motion analysis; time series; video signal processing; abnormal human gait classification; decision tree; gait video; human gait recognition; human identification; joint angle; normal human gait classification; silhouette image extraction; swing distance; time series shapelets; Data mining; Feature extraction; Humans; Joints; Legged locomotion; Time series analysis; Vectors; Gait; Keypose; Shapelets; Silhouette;
Conference_Titel :
Advances in Computing and Communications (ICACC), 2012 International Conference on
Conference_Location :
Cochin, Kerala
Print_ISBN :
978-1-4673-1911-9
DOI :
10.1109/ICACC.2012.8