Classification of Action Based Video using Heterogeneous Feature Extraction and SVM
Chandrawal kaur1, Amit Doegar2

1Chandrawal Kaur, Computer Science and Engineering Department, NITTTR Chandigarh, India.
2Amit Doegar, Computer Science and Engineering Department, NITTTR Chandigarh, India.

Manuscript received on 25 August 2019. | Revised Manuscript received on 06 September 2019. | Manuscript published on 30 September 2019. | PP: 1887-1892 | Volume-8 Issue-11, September 2019. | Retrieval Number: K20890981119/2019©BEIESP | DOI: 10.35940/ijitee.K2089.0981119
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Abstract: Action recognition (AR) plays a fundamental role in computer vision and video analysis. We are witnessing an astronomical increase of video data on the web and it is difficult to recognize the action in video due to different view point of camera. For AR in video sequence, it depends upon appearance in frame and optical flow in frames of video. In video spatial and temporal components of video frames features play integral role for better classification of action in videos. In the proposed system, RGB frames and optical flow frames are used for AR with the help of Convolutional Neural Network (CNN) pre-trained model Alex-Net extract features from fc7 layer. Support vector machine (SVM) classifier is used for the classification of AR in videos. For classification purpose, HMDB51 dataset have been used which includes 51 Classes of human action. The dataset is divided into 51 action categories. Using SVM classifier, extracted features are used for classification and achieved best result 95.6% accuracy as compared to other techniques of the state-of- art.
Keywords: Action Recognition, Classification, Convolution neural network, SVM.
Scope of the Article: Classification