Multiview Detection with Cardboard Human Modeling

Code and data for paper:
Multiview Detection with Cardboard Human Modeling
Jiahao Ma*, Zicheng Duan*, Liang Zheng, Chuong Nguyen
[Paper and Appendices]

Overview

We propose a new state-of-the-art method MvCHM for multiview object detection, and some major upgrades on existing multiview pedestrian detectiom benchmarks Wildtrack dataset and MultiviewX dataset, respectively named as Wildtrack+ and MultiviewX+.

TODOs

• Inference Code and Pretrained Models
• Detailed Instructions
• Datasets
• Training Codes
• Results and More visualizations

Prerequisites

This project is tested to run on environment with:

• CUDA > 11
• torchvision 0.11.3
• Windowns 10/11, Ubuntu 20.04

Installation

1. Create a new conda environment named mvchm to run this project:

conda create -n mvchm python=3.9.7
conda activate mvchm

2. Make sure your system meet the CUDA requirements and install some core packages:

pip install easydict torch==1.12.1+cu113 torchvision==0.13.1+cu113 tqdm scipy opencv-python

3. Clone this repository

cd Your-Project-Folder
gir clone git@github.com:ZichengDuan/MvCHM.git

Data Preperation

Datasets

Wildtrack and MultiviewX

Wildtrack+

MultiviewX+