Abstract
<abstract>
<p>Human motion recognition is of great value in the fields of intelligent monitoring systems, driver assistance system, advanced human-computer interaction, human motion analysis, image and video processing. However, the current human motion recognition methods have the problem of poor recognition effect. Therefore, we propose a human motion recognition method based on Nano complementary metal oxide semiconductor (CMOS) image sensor. First, using the Nano-CMOS image sensor to transform and process the human motion image, and combines the background mixed model of pixels in the human motion image to extract the human motion features, and feature selection is conducted. Second, according to the three-dimensional scanning features of Nano-CMOS image sensor, the human joint coordinate information data is collected, the state variables of human motion are sensed by the sensor, and the human motion model is constructed according to the measurement matrix of human motions. Finally, the foreground features of human motion images are obtained by calculating the feature parameters of each motion gesture. According to the posterior conditional probability of human motion images, the recognition objective function of human motion is obtained to realize human motion recognition. The results show that the human motion recognition effect of the proposed method is good, the extraction accuracy is high, the average human motion recognition rate is 92%, the classification accuracy is high, and the recognition speed is up to 186 frames/s.</p>
</abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
Cited by
1 articles.
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1. Exploring Human Activity Patterns: Investigating Feature Extraction Techniques for Improved Recognition with ANN;2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP);2024-07-11