Improving the Accuracy of Single-Pixel Tracking a Small-Sized Moving Object Using Defocus Blur

Geometric moment patterns are selected as non-orthogonal gray patterns used for single-pixel centroid detection. By using geometric moment patterns for structured illumination, the reflected light intensities collected by a single-pixel detector correspond to the zero-order and first-order geometric moments, which are leveraged to reconstruct and track the centroid of moving objects. However, the frame rate of the single-pixel tracking system is limited because geometric moment patterns are grayscale while the digital micromirror device (DMD) performs binary modulation much faster than grayscale modulation. Although spatial dithering addressed the problem by binarizing the patterns, the accuracy of tracking a small-sized moving object will decrease by the extra dithering error. Therefore, we propose a binary defocus method to improve the accuracy of tracking a small-sized moving object while ensuring a high frame rate. The key to the proposed method is to simulate grayscale geometric moment patterns by defocusing projection binary geometric moment patterns. The experimental results show that the proposed defocus method is still available for tracking small-sized moving objects even when the focused method fails, and ∼48% lower Centroid Estimation Error (CEE) than that of the focused approach when the frame rate is ∼7400 Hz. Meanwhile, our works also provide relationships between CEE of focused and defocus methods varies with the size of the moving object.