Improving the quality of building space depths maps using multi-area active-pulse television measuring systems in dynamic scenes

The purpose of this work is software implementation of the temporal frame interpolation, the formation of selection criteria and the choice of a suitable neural network model based on the obtained practical data. And also, evaluation of its efficiency for eliminating the interframe shift effect of dynamic objects on the depth maps of multi-area active-pulse television measuring systems in order to improve the accuracy of map building. As initial data for the experiments, static frames were recorded while moving the test rig along the X and Z axes. The static frames are images of the test rig, averaged 100 times, at a distance of 13 meters, which moved along an automated linear guide with a step of 1 mm. As a result of the work, an assessment of the interframe shift effect influence on space depth maps of multi-area active-pulse television measuring systems containing dynamic objects was made. The implementation and testing of the temporal frame interpolation algorithm for suppressing the interframe shift effect of dynamic objects on depth maps was also performed. The algorithm was implemented using Python and the PyCharm IDE with SciPy, NumPy, OpenCV, PyTorch, Threading and other libraries. Numerical values of the RMSE, PSNR, and SSIM metrics were obtained before and after eliminating the effect of interframe shift of dynamic objects on depth maps. The use of the temporal frame interpolation algorithm allows more accurate measurement of distance to moving object in the field of view of multi-area active-pulse television measuring systems.