Research and Simulation of High Frame Rate Real-time Image Tracking Technology Based on Compressed Sensing

In recent years, the compressed sensing theory in the field of signal processing has broken the Nyquist sampling theorem that the signal sampling rate must be more than twice the signal bandwidth in the traditional sampling process to accurately reconstruct the original signal, which greatly reduces the cost of information storage, processing and transmission. Target tracking is an important research topic in the field of computer vision. Target tracking has a great impact on human life and has been widely used. For example, security monitoring, human-computer interaction, military field and so on, but there are still many challenges in target tracking, such as illumination intensity change, background disturbance, occlusion, scale change and so on. Target tracking technology is a typical application of computer image vision technology. Aiming at the problem of large amount of computation and poor real-time performance in the process of video image target tracking, this paper uses compressed sensing algorithm to reduce the dimension of target features to reduce the amount of computation, and combines naive Bayesian classifier to recognize the target. Finally, this paper presents a tracking system using compressed sensing algorithm to sparse express target features. Through experiments, it can be found that the system has high tracking accuracy.