Detection Algorithm Based On Compressed Sensing Of Moving Targets

Compressed sensing has attracted considerable attention in areas of applied mathematics, computer science, and electrical engineering by suggesting that it may be possible to surpass the traditional limits of sampling theory, take full advantage of the fact that the natural signals are sparse or compressible. Compressed sensing theory asserts that one can recover certain sparse or compressible signals from far fewer measurements with nonlinear optimization methods. At present, how to design measurement programs effectively and extend compressed sensing theory to the practical data models and the acquisition system is recent hot fields.This paper focuses on how to use the compressed sensing theory for moving target detection. The main tasks are as follows:The analysis of the compressed sensing theory. Studied the compressed sensing theory and the analyzed theoretical basis of compressed sensing. Summarized the existing measurement matrix and signal reconstruction method.The analysis of the classical motion detection. Analyzed and studied the classical motion detection research. Summarized the advantages and disadvantages of each method and the application environment.The research of compressed sensing method for motion detection. Based on the studies and analyses of compressed sensing and classical motion detection, proposed a compressed sensing background subtraction method for motion detection. Experiments show that this algorithm has batter performance, and the experimental results verify the feasibility of the method.Compressed sensing has the character of simple calculation, and sensing and compressing data simultaneously. It can reduce the cost of calculation and storage during the process of motion detection. It is convenient for the application, such as surveillance, which has higher requirements for computing power and power consumption.