Human Motion Tracking with Less Constraint of Initial Posture from a Single RGB-D Sensor

Abstract

High-quality and complete human motion 4D reconstruction is of great significance for immersive VR and even human operation. However, it has inevitable self-scanning constraints, and tracking under monocular settings also has strict restrictions. In this paper, we propose a human motion capture system combined with human priors and performance capture that only uses a single RGB-D sensor. To break the self-scanning constraint, we generated a complete mesh only using the front view input to initialize the geometric capture. In order to construct a correct warping field, most previous methods initialize their systems in a strict way. To maintain high fidelity while increasing the easiness of the system, we updated the model while capturing motion. Additionally, we blended in human priors in order to improve the reliability of model warping. Extensive experiments demonstrated that our method can be used more comfortably while maintaining credible geometric warping and remaining free of self-scanning constraints.

An Advanced Noise Reduction and Edge Enhancement Algorithm

“…Noise usually accompanies images during acquisition or transmission, resulting in contrast reduction, color shift, and poor visual quality. The interference of noise not only contaminates the naturalness of an image, but also damages the precision of various computer vision-based applications, such as semantic segmentation [ 1 , 2 ], motion tracking [ 3 , 4 ], action recognition [ 5 , 6 ], and object detection [ 7 , 8 , 9 , 10 , 11 , 12 ], to name a few. Consequently, noise removal for these applications has attracted great interest as a preprocessing task over the last two decades.…”

Trajectory Method for Defense Human Motion Posture Based on Nano-Sensor

“…Nano-sensor [14][15] shows a gradual decline in data mining and transmission. The first nano-sensor transmits the symbol sequence, namely HG1-1011, and the second sensor is HG2-1001.…”