Ultra-efficient single-pixel tracking and imaging of moving objects based on geometric moment

Single-pixel fast-moving object tracking technique has potential applications in autonomous driving. However, employing a single pixel detector to the dynamic object imaging is a challenging task due to severe motion blur. Existing motion compensation methods are computationally expensive since they require the reconstruction of a series of images corresponding to the object moving process at different time intervals. In this paper, we propose a novel method that only needs to reconstruct one image of the object during the whole moving process, thus significantly reducing the computational cost. We achieve ultra-efficient simultaneously tracking and imaging of a moving object by designing novel illumination patterns. With our designed patterns, there is a mathematical similarity between the single-pixel imaging model and the low-order moment of the image. Thus, the detected light intensity can be directly used as the low-order moment value to calculate the position of the object. This work only needs two patterns for object localization in each frame, which is the current highest positioning frame rate in the single-pixel imaging field. For a 128×128 pixels object, with the employment of the Compressed Sensing (CS) and Total Variation Augmented Lagrange Alternative Direction Algorithm (TVAL3), the proposed new method can reconstruct a high-quality image with a sampling rate of 12.2% without any priori motion information. The proposed scheme paves the way for the development of fast-moving object imaging.