Infrared Small Target Detection And Tracking Based On Image Sparse Representation

With the development of modern science, infrared related techniques have been extensively used and infrared image processing and recognition also play a more and more important role. Because of far distance when equipment capturing object and large noise in the environment, small target detection and tracking is always a challenge area.Recently, compressed sensing which is based on sparse representation theory attracts huge attentions. Instead of being constrained to traditional basis construction such as Fourier transform and wavelet transform, sparse representation uses overcomplete dictionary to represent signal. A normal overcomplete dictionary always obtain more atoms than the dimension of itself, and therefore sparse representation search for the least number of atoms in a single representation. Although the theory is still not fully explored and need for further improvement, lots of its applications reach excellent result and imply a promising future.Based on such background, this paper approaches small target detection and tracking via sparse representation. The proposed algorithms are as follows:1. Apply sparse representation theory to obtain sparse coefficients of testing image based on a small target dictionary built by Modified Gaussian Intensity Model. Then, taking advantage of the difference between representations of target and background sub-images, sparse concentration index can be used to describe target distribution in the image. Finally, a simple threshold can easily reveal target class and improve detection performance. 2. Furthermore, train an optimal small target dictionary ground on research of dictionary learning. Upon that, the best representation of the dictionary has ability to rebuild image in a high accuracy and similarity. As a result, residual between original and rebuilt image could achive higher signal-to-noise ratio by background and clutter suppression.3. Benifit from outstanding antinoise ability and occlusion insensitivity of sparse representation, a new infrared target tracking technique is proposed under particle filter framework. The algorithm employs sparse representation as observation model which is implemented by building target and noise subspace, and successfully enhances object decription outcome. Meanwhile, online learning is used to dynamically update the target subspace in order to adapt to background variation and improve the efficiency and robust of the algorithm.