Authors:
Muhammad Hameed Siddiqi
1
;
La The Vinh
1
and
Adil Mehmood Khan
2
Affiliations:
1
Kyung Hee University, Korea, Republic of
;
2
Ajou University, Korea, Republic of
Keyword(s):
Activity Recognition, Wavelet Transform, HCRF, Video Surveillance.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
In this research, we proposed testing and validating the accuracy of employing wavelet transform and Hidden Conditional Random Field (HRCF) for video based activity recognition. For feature extraction, Symlet wavelet was tested and decomposed up to 4 levels, and some of the highest coefficients were extracted from each level of decomposition. These coefficients were based on the average frequency of each video frame and the time difference between each frame. Finally, a novel HRCF model was applied for recognition. The proposed method was tested on a database of ten activities, where the data were collected from nine different people, and compared with one of the existing techniques. The overall recognition rate, using the symlet wavelet family (Symlet 4), was 93% that showed an improvement of 13% in performance.