DocumentCode :
1703258
Title :
Detection and Classification of Repetitious Human Motions Combining Shift Variant and Invariant Features
Author :
Ide, Ichiro ; Kuhara, Taku ; Deguchi, Daisuke ; Takahashi, Tomokazu ; Murase, Hiroshi
Author_Institution :
Grad. Sch. of Inf. Sci., Nagoya Univ., Nagoya, Japan
fYear :
2012
Firstpage :
86
Lastpage :
89
Abstract :
Detection and classification of significant human motions are important tasks when analyzing a video that records human activities. Among various human motions, we consider that repetitious motions are specially important since they are usually results of activities with clear intentions. In this paper, we propose and evaluate a method that detects video segments that contain repetitious motions, which is robust to motion shift. Experimental results showed the effectiveness of the proposed method compared to conventional methods. In addition, we report a preliminary result of an experiment on the classification of the types of the detected repetitious motions.
Keywords :
feature extraction; gait analysis; image classification; image segmentation; motion estimation; video signal processing; human activity recording; motion shift robustness; repetitious human motion classification; repetitious human motion detection; shift invariant features; shift variant features; video analysis; video segment detection; Accuracy; Feature extraction; Humans; Motion segmentation; Multimedia communication; Training; Vectors; Repetitious motions; classification; detection; human motions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Security Technologies (EST), 2012 Third International Conference on
Conference_Location :
Lisbon
Print_ISBN :
978-1-4673-2448-9
Type :
conf
DOI :
10.1109/EST.2012.7
Filename :
6328089
Link To Document :
بازگشت