Human Action Recognition using STIP Techniques
H S Mohana1, Mahathesha U2

1Mr. Mahanthesha U*, Research Scholar, Department of Electronics and Instrumentation Engineeing, Malnad College of Engineering, Hassan, Karnataka, India.
2Dr H S Mohana, Vice Principal, Professor & Head, Department of Electronics and Communication Engineering, Navkis College of Engineering, Hassan, Karnataka, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 30, 2020. | Manuscript published on May 10, 2020. | PP: 878-873 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5482059720/2020©BEIESP | DOI: 10.35940/ijitee.G5482.059720
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Abstract: The activities of human can be classified into human actions, interactions, object- human interactions and group actions. The recognition of actions in the input video is very much useful in computer vision technology. This system gives application to develop a model that can detect and recognize the actions. The variety of HAR applications are Surveillance environment systems, healthcare systems, Military, patient monitoring systems (PMS), etc., that involve interactions between electronic devices such as human-computer interfaces with persons. Initially collected the videos containing actions or interactions were performed by the humans. The given input videos were converted into number of frames and then these frames were undergone preprocessing stage using by applying median filter. The median filter identifies the noises present in the frame and then which replaces the noise by the median of the neighboring pixels. Through frames desired features were extracted. The recognize of action present in the person of the video using these extracted features. There are three spatial temporal interest point (STIP) techniques such as Harris SPIT, Gabour SPIT and HOG SPIT were used for feature extraction from video frames. SVM algorithm is applied for classifying the extracted feature. The action recognition is based on the colored label identified by classifier. The system performance is measured by calculating the classifier performance which is the Accuracy, Sensitivity and Specificity. The accuracy represents the classifier reliability. The specificity and sensitivity represents how exactly the classifier categorizes it’s features to each correct category and how the classifier rejects the features that are not belonging to the particular correct category. 
Keywords: Action recognition, STIP, Harris filter, Gabour Filter. Histogram Orient Gradient (HOG)
Scope of the Article: Pattern Recognition and Analysis