In the task of human action recognition in uncontrolled video, motion features are used widely
in order to achieve subject and appearence invariance. We implemented 3 Histograms of
Oriented Optical Flow based method which have a common motion feature extraction phase.
We compute an optical flow field over each frame of the video. Then those flow vectors
are histogrammed due to angle values to represent each frame with a histogram. In order to
capture local motions, The bounding box of the subject is divided into grids and the angle
histograms of all grids are concetanated to obtain the final motion feature vector. Motion
Features are supplied to 3 dierent classification system alternatives containing clustering
combined with HMM, clustering with K-nearest neighbours and average histograms methods.
Three methods are implemented and results are evaluated over Weizmann and KTH datasets.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12615060/index.pdf |
| Date | 01 September 2012 |
| Creators | Ercis, Firat |
| Contributors | Ulusoy Parnas, Ilkay |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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