2D to 3D pedestrian detection algorithm based on deep learning

Abstract

Based on the actual demand of the public for 3D content generation technology and targeting the problems existing in traditional 2D to 3D image conversion methods, we propose a 2D to 3D pedestrian detection algorithm based on deep learning. The algorithm uses object segmentation to first extract the human body's 2D outline from the input image. It then combines shallow features of the image to use a fully connected layer to map latent vectors to low-dimensional mesh vertex feature vectors, ultimately generating a triangle mesh for the 3D model. By alternately using SpiralConv and upsampling operations, the 3D mesh of the pedestrian can be generated more accurately. Experimental results show that the algorithm can accurately construct a 3D model of a pedestrian and has real-time feedback capability. The algorithm is suitable for real-time monitoring scenarios such as action recognition. Compared to traditional 2D to 3D image conversion methods, the algorithm uses deep learning to automatically learn features, which has higher accuracy and transferability. Additionally, the algorithm extracts 2D contour information of the human body using object segmentation, which improves the accuracy of generating 3D meshes. We believe that this algorithm has an important role to play in the development of 3D content generation technology.