# Real-time Street Human Motion Capture

arXiv: https://arxiv.org/abs/2112.11543 · HTML: https://mlsanity.com/paper/2112.11543/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [TACO: Trash Annotations in Context for Litter Detection](https://mlsanity.com/paper/2003.06975/) (2020)
- [Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos](https://mlsanity.com/paper/1808.04702/) (2018)
- [Segmentation of Arterial Walls in Intravascular Ultrasound Cross-Sectional Images Using Extremal Region Selection](https://mlsanity.com/paper/1806.03695/) (2018)
- [IVUS-Net: An Intravascular Ultrasound Segmentation Network](https://mlsanity.com/paper/1806.03583/) (2018)

## Abstract (arXiv)

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.