Real-time Street Human Motion Capture

TL;DR

This paper presents a real-time street scene human motion capture system using neural networks, enabling 3D animation and street condition analysis for applications like self-driving cars.

Contribution

It introduces a neural network-based method for real-time human motion capture in street scenes integrated with Unity.

Findings

Achieved real-time motion capture in street scenes

Enabled 3D human animation from video data

Provided insights into street condition estimation

Abstract

In recent years, motion capture technology using computers has developed rapidly. Because of its high efficiency and excellent performance, it replaces many traditional methods and is being widely used in many fields. Our project is about street scene video human motion capturing and analysis. The primary goal of the project is to capture the human motion in a video and use the motion information for 3D animation (human) in real-time. We applied a neural network for motion capture and implement it in the unity under a street view scene. By analyzing the motion data, we will have a better estimation of the street condition, which is useful for other high-tech applications such as self-driving cars.