000			06738nam a22002537a 4500
008			210112b2013 a|||f mb|| 00| 0 eng d
040			_aEG-CaNU _cEG-CaNU
041	0		_aeng _beng
082			_a610
100	0		_aFadwa Fawzy Fouad _9276
245	1		_aHuman Action Recognition in Videos Employing 2-DPCA on 2DHHOF and Radon Transform / _cFadwa Fawzy Fouad
260			_c2013
300			_a 90 p. _bill. _c21 cm.
500			_3Supervisor: Mohamed A. El-Helw
502			_aThesis (M.A.)—Nile University, Egypt, 2013.
504			_a"Includes bibliographical references"
505	0		_aContents: \1 Introduction 1 1.1 Challenges and characteristics of the domain . . . . . . . . . . . . . . . . . . 2 1.1.1 inter- and Intra-class variations . . . . . . . . . . . . . . . . . . . . . 2 1.1.2 Environmental Variations and Capturing conditions . . . . . . . . . . 2 1.1.3 Temporal variations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Proposed Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Overview 5 2.1 Input videos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Human detection/segmentation . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.1 Background subtraction . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.2 Optical Flow (OF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.3 Histogram of Oriented Gradients (HOG) . . . . . . . . . . . . . . . . 10 2.3 Feature Extraction and Modeling . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3.1 Non-Parametric Approaches . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.2 Volumetric Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.3 Parametric Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4 Action Classication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4.1 Direct classication . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4.2 Temporal state-space models . . . . . . . . . . . . . . . . . . . . . . . 16 3 2DHOOF/2DPCA Contour Based Optical Flow Algorithm 18 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 vi 3.2 What 2DHOOF Features are . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2.1 Optical Flow Techniques . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2.2 Alignment issues with OF . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2.3 The Calculation of 2D Histogram of Optical Flow(2DHOOF) . . . . . 23 3.3 Overall System Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3.1 Training Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3.2 Testing Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.4 Used Data-sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.4.1 Weizmann Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.4.2 IXMAS Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.5 Experimental Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.1 Weizmann Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.2 IXMAS Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4 Human Gesture Recognition Employing Radon Transform/2DPCA 48 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.2 Radon Transform (RT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.3 Overall System Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3.1 Training Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.3.2 Testing Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.4 Used Data-sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.4.1 ChaLearn Gesture Data . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.5 Experimental Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . 55 4.5.1 Input videos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.5.2 Segmentation and Blob extraction . . . . . . . . . . . . . . . . . . . . 55 4.5.3 Alignment using RT of the First Frame . . . . . . . . . . . . . . . . . 60 4.5.4 Video Chopping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.5.5 Calculate the MEI and MHI . . . . . . . . . . . . . . . . . . . . . . . 66 4.5.6 Get Radon Transform for MEI and MHI . . . . . . . . . . . . . . . . 68 4.5.7 2DPCA and Feature Extraction . . . . . . . . . . . . . . . . . . . . . 69 vii 5 Conclusion 73 5.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.2 Future Work . .
520	3		_aAbstract: Human action recognition is a very important trend of research with a great interest in computer vision area. It has applications in various elds such as surveillance, content-based video retrieval, gesture recognition and sports analysis. The speed, storage requirements, and accuracy of the human action recognition system are highly dependent on the extracted features, and modeling approaches. All dierent recognition techniques aim to extract the most representative and accurate features with low storage requirements. In this thesis, two algorithms are proposed for human action recognition which target the high accuracy and the low computation complexity. For the rst algorithm the novel Two Dimensions Histogram of Oriented Optical ow (2DHOOF) features where extracted using the actor's contour. Two Dimensions Principal Component Analysis (2DPCA) was used for feature extraction. The Euclidean Distance Classier was used for the nal classication decision. Experimental results applied on Weizmann and the IXMAS datasets achieved the highest reported recognition accuracy and the fastest runtime compared to recent methods. In the second algorithm Radon Transform (RT) was extracted from the moving parts of the actor. Using RT enables us to easily align the actor's silhouette with the center of the frame without the need of further pre-processing. 2DPCA was used for feature extraction. The Euclidean Distance Classier was used for the nal classication decision. Experimental results applied on one shot learning ChaLearn Gesture Data-set achieved high recognition accuracy and the fast runtime. Experimental results promote our algorithms for real time applications.
546			_aText in English, abstracts in English.
650		4	_aInformatics-IFM _9266
655		7	_2NULIB _aDissertation, Academic _9187
690			_aInformatics-IFM _9266
942			_2ddc _cTH
999			_c8807 _d8807