Real-time target recognition and multi-motion parameters acquisition via single-pixel detection.

Real-time target recognition and multi-motion parameters acquisition are critical in many applications. Conventional imaging methods struggle to achieve simultaneous high-precision recognition and motion analysis due to excessive data requirements, low update rates, and dynamic deformation interference. This paper proposes an image-free framework utilizing single-pixel detection, where six coding masks simultaneously compute affine moment invariants resistant to affine distortions (translation, rotation, scaling, shearing) and extract multiple motion parameters (centroid, rotation angle, scaling factor). The simulation results demonstrate that the affine invariants enable stable recognition of different aircraft. We further designed dynamic experiments to detect objects undergoing various affine transformations, which maintains robustness with a deviation coefficient of 2.12% and achieves accurate recognition of diverse targets at one-sixth of the DMD's maximum flipping frequency, approximately 3 kHz. Furthermore, the proposed method realizes a centroid localization accuracy of 1.12 pixels, a rotation error below 1.3°, and a scaling factor accuracy of 0.04. To our knowledge, this work represents the first demonstration of direct recognition of moving objects with shear distortion using single-pixel detection without performing image reconstruction. The proposed framework offers a novel solution for synchronous target recognition and motion analysis of high-speed targets with great potential in applications such as remote sensing and security monitoring, real-time tracking of fast-moving objects, and all-optical computing.