Non-imaging real-time detection and tracking of fast-moving objects using a single-pixel detector

Detection and tracking of fast-moving objects have widespread utility in many fields. However, fulfilling this demand for fast and efficient detecting and tracking using image-based techniques is problematic, owing to the complex calculations and limited data processing capabilities. To tackle this problem, we propose an image-free method to achieve real-time detection and tracking of fast-moving objects. It employs the Hadamard pattern to illuminate the fast-moving object by a spatial light modulator, in which the resulting light signal is collected by a single-pixel detector. The single-pixel measurement values are directly used to reconstruct the position information without image reconstruction. Furthermore, a new sampling method is used to optimize the pattern projection way for achieving an ultra-low sampling rate. Compared with the state-of-the-art methods, our approach is not only capable of handling real-time detection and tracking, but also it has a small amount of calculation and high efficiency. We experimentally demonstrate that the proposed method, using a 22kHz digital micro-mirror device, can implement a 105fps frame rate at a 1.28% sampling rate when tracks. Our method breaks through the traditional tracking ways, which can implement the object real-time tracking without image reconstruction.

Non-imaging real-time detection and tracking of fast-moving objects

Real-time detection and tracking of fast-moving objects have achieved great success in various fields. However, many existing methods, especially low-cost ones, are difficult to achieve real-time and long-term object detection and tracking. Here, a non-imaging strategy is proposed, including two stages, to realize fast-moving object detection and tracking in real-time and for the long term: 1) a contour-moments-based method is proposed to optimize the Hadamard pattern sequence. And then reconstructing projection curves of the object based on single-pixel imaging technology. The projection curve, which including the object location information, is reconstructed directly with the measurements collected by a single-pixel detector; 2) The fastest changing position in the projection curve can be obtained by solving first-order gradients. A gradient differential is used in two first-order gradients to calculate a differential curve with the sudden change positions. Finally, we can obtain the boundary information of the fast-moving object. We experimentally demonstrate that our approach can achieve a temporal resolution of 105 frames per second at a 1.28% sampling rate by using a 22,000 Hz digital micro-mirror device. The detection and tracking algorithm of the proposed strategy is computationally efficient. Compared with the state-of-the-art methods, our approach can make the sampling rate lower. Additionally, the strategy acquires not more than 1MB of data for each frame, which is capable of fast-moving object real-time and long-term detection and tracking.

PHI-MVS: Plane Hypothesis Inference Multi-view Stereo for Large-Scale Scene Reconstruction

PatchMatch based Multi-view Stereo (MVS) algorithms have achieved great success in large-scale scene reconstruction tasks. However, reconstruction of texture-less planes often fails as similarity measurement methods may become ineffective on these regions. Thus, a new plane hypothesis inference strategy is proposed to handle the above issue. The procedure consists of two steps: First, multiple plane hypotheses are generated using filtered initial depth maps on regions that are not successfully recovered; Second, depth hypotheses are selected using Markov Random Field (MRF). The strategy can significantly improve the completeness of reconstruction results with only acceptable computing time increasing. Besides, a new acceleration scheme similar to dilated convolution can speed up the depth map estimating process with only a slight influence on the reconstruction. We integrated the above ideas into a new MVS pipeline, Plane Hypothesis Inference Multi-view Stereo (PHI-MVS). The result of PHI-MVS is validated on ETH3D public benchmarks, and it demonstrates competing performance against the state-of-the-art.