|
The detection of small and dim targets in infrared image sequences is a key technology in infrared search and tracking systems. The optimization method based on low-rank and sparse decomposition is the mainstream research method in this field. Most of the existing optimization methods are based on a single frame, only exploiting spatial information but ignoring the time dimension. The multi-frame detection method makes full use of temporal and spatial information to achieve higher accuracy. However, the existing multi-frame detection method is very slow, because they rely on slow decomposition methods. To solve the problem, we propose the infrared sequence tensor model for multi-frame detection. First, we select the tensor average rank to describe the low-rank property of the infrared sequence. It can be solved quickly. Second, we introduce a new approach to extract prior information as a prior weight tensor, which can highlight the target and suppress noise and strong edges on the background. Third, we formulate the optimization equation based on the tensor average rank and prior weight, which can be solved efficiently with tensor robust principal component analysis(TRPCA). Experiments show that our proposed method has high detection accuracy. The speed of our proposed method is much faster than that of the current multi-frame optimization methods and is comparable to that of the singleframe optimization methods.
|
