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Abstract
Human activity recognition (HAR) is a fascinating and significant challenging task. Generally, the accuracy of HAR systems relies on the best features from the input frames. Mostly, the activity frames have the hostile noisy conditions that cannot be handled by most of the existing edge operators. In this paper, we have designed an adoptive feature extraction method based on edge detection for HAR systems. The proposed method calculates the direction of the edges under the presence of nonmaximum conquest. The benefits are in ease that depends upon the modest procedures, and the extension possibility is to determine other types of features. Normally, it is practical to extract extra low-level information in the form of features when determining the shapes and to get the appropriate information, the additional cultured shape detection procedure is utilized or discarded. Basically, this method enlarges the percentage of the product of the signal-to-noise ratio (SNR) and the highest isolation along with localization. During the processing of the frames, again some edges are demonstrated as a footstep function; the proposed approach might give better performance than other operators. The appropriate information is extracted to form feature vector, which further be fed to the classifier for activity recognition. We assess the performance of the proposed edge-based feature extraction method under the depth dataset having thirteen various kinds of actions in a comprehensive experimental setup.
Highlights
Computational Intelligence and Neuroscience gyroscopes decreases the ease of human body and terminates the spontaneity of human computer interaction [6]
In this paper, we have designed an adoptive feature extraction method for Human activity recognition (HAR) systems. e proposed method calculates the direction of the edges under the presence of nonmaximum conquest. e benefits are in ease that depends upon the modest procedures, and the extension possibility is to determine other types of features
During the processing of the frames, again some edges are demonstrated as a footstep function; the proposed approach might give better performance than other operators. e appropriate information is extracted to form feature vector, which is further fed to the classifier for activity recognition
Summary
Computational Intelligence and Neuroscience gyroscopes decreases the ease of human body and terminates the spontaneity of human computer interaction [6]. A huge amount of work has been done for segmentation and recognition; very limited approaches have been proposed on feature extraction and selection. One of the major problems in skeleton-based feature extraction method is that the alone sparse skeleton data might not enough to completely classify the human activities [31].
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