Sparse Representation based Target Detection in Infrared Image

Abstract

Infrared target detection research has recently attracted much attention, especially in pedestrian detection. In this paper, we use compressive sensing theory, a new and emerging field, for an infrared target detection system. Based on the framework of Bayesian filter, target is expressed by sparse representation. Compressive sensing (CS) is based on the illusion that a small quantity of non-adaptive linear projection of a compressible signal contains sufficient information for signal reconstruction and processing. In this paper we give an overview of compressive sensing and our proposed method. The method firstly construct appearance model using features extracted from the data independent image feature space. The appearance model can preserve structure targets’ feature since it adopts non-adaptive random projections. The experiment results show that the proposed infrared target detection system is feasible and efficient.