Method of visual and infrared fusion for moving object detection

Abstract

A method based on low rank and sparse decomposition is proposed for moving object detection by the fusion of visual and infrared video. The visual and infrared image sequences are decomposed into the joint low rank background term, the uncorrelated sparse moving nonobject term, and the common sparse moving object term via a joint minimization cost of nuclear norm, F norm, and l(1) norm. This method provides a flexible framework that can easily fuse information from visual and infrared video. The prior fusion strategies are not required. The complementary information on visual and infrared images can be naturally fused in the procedure of object detection. The experimental results show that the proposed algorithm is effective.

A Thermal Infrared and Visible Images Fusion Based Approach for Multitarget Detection under Complex Environment

“…Thirdly, seven Hu invariant moments of visible image in different perspectives are calculated, respectively. Thus, the BBA values of the visible images can be obtained by (9) and (10), which is listed in Table 1 (see ( )). The BBA values of the visible images and the thermal infrared images.…”

“…The instability of the measured image signals will make the useful signals submerged in the background. There would be uneven distribution of gray, detail blurred, and poor contrast ratio in visual image and lower signal-to-noise ratio (SNR), the halo effect, silhouette, and fuzzy edge in thermal image [8,9]. Ignoring these imperfections and making unrealistic assumption will lead to untrustworthy inferences.…”

Multi-Focus Image Fusion using Self-Similarity and Depth Information in Nonsubsampled Shearlet Transform Domain

“…The spatial domain algorithms mainly include principal component analysis [4,5], guided filtering based method [6] and so on. The transform domain algorithms are mainly based on multiresolution geometric analysis (MGA) tool domain, such as image fusion algorithm based on wavelet [7,8], ripplet [9], contourlet [10][11][12], shearlet [13,14], surfacelet [15], trained dictionaries [16,17].…”

Infrared and visible image fusion with spectral graph wavelet transform

“…Using infrared and visible image fusion technique, we not only can overcome the limitation of information obtained from individual sensors but also can achieve improved observation. Therefore, infrared and visible image fusion is an important technique to enhance the value of the image fusion technique in various fields, such as object detection [4], object tracking [5], face recognition [6], hiding [7], and securing multiple images [8].…”