Reporting and Visualization for Hazardous Events

Overview

IncidenceReporting

Reporting and Visualization for Hazardous Events

Web Page

Problem Statement

Create a solution that spreads awareness of, visualizes the importance of, and/or decreases the time-to-action of safety-related incidents or topics.

Example safety-related incidents: 1) Identification and reporting of tripping hazards 2) Personnel near heavy machinery 3) Escape of toxic gases

Map Visualization

Solution

Video data is collected to detect if someone has fallen, or if personnel are too near heavy machinery, or if another safety-related incident has happened. If someone is too near heavy machinery, a notification will be sent out to them. Additionally, we have a webpage to visualize safety incidents geographically and topically, and we also have a place for people to write anonymous incidence reports.

Pose Estimation

Description

Inspiration for your submission

  • Our inspiration came from Conoco Phillip's Spirit Values. We wanted to create something that represented these values and we believe our application does an excellent representation.
  • SAFETY - Safety is the core idea of our application. We wanted to provide an objective solution to safety.
  • PEOPLE - The people are what drives this application. We gave them the power to view and upload data.
  • INTEGRITY - Integrity is extremely important and this application provides transparency to our people.
  • RESPONSIBILITY - We believe everyone is responsible and this will provide employees with more accountability.
  • INNOVATION - Innovation is what powers this application our fall detection will help us identify safety issues throughout Conoco Phillips.
  • TEAMWORK - Together we can create technology to keep us safe and responsible.

What your submission does

  • Tripping hazards and heavy machinery incident reporting web app
  • Automatic incident reporting
  • Automatic fall detection and reporting using ML
  • Fall recording logging using EchoAR

How you built it

  • Streamlit
  • python
  • C++
  • Firebase

Challenges that you ran into, and how you overcame them

  • Machine Learning Model was tricky to set up
  • Map was not working for a long time

Accomplishments that you're proud of

  • Elegant UI
  • Email integration
  • Pose Estimation

What's next for your product?

  • Integration with CCTV cameras
  • adapt the solution to companies other than ConocoPhillips. We feel our solution can be applied to any company with workplace safety issues
  • Our team split up the work to most efficiently produce a working front end and back end. The front end of our product was constructed using Streamlit, which is a web-app framework that writes markdown from python. This allowed us to easily construct forms and display data in a visually easy-to-digest way for the user. Our data would be stored on a local SQL database such as MySQL, which was able to communicate with our product through the MySQL-Connector python library. Finally, our product also includes a machine learning model that can detect incidents and automatically report them and store the data in the database.

Challenges that you ran into, and how you overcame them

  • We faced adversities when attempting to integrate the different parts of our product such as the database, machine learning, and the front-end data visualization components.

Accomplishments that you're proud of

  • We are proud of producing a Minimum Viable Product that we could showcase to the company and that we believe suits a broad set of use-cases.

What's next for your product?

  • In the future, we hope to update our product in terms of specificity and scale. Some ideas that encompass this could include more incident types and more complex query abilities for the user.

Running the app

streamlit run main.py

Running Machine Learning Algorithm

cd Human-Falling-Detect-Tracks/
python main.py --device=cpu
Owner
Jv Kyle Eclarin
snakes and letters
Jv Kyle Eclarin
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences

Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences 1. Introduction This project is for paper Model-free Vehicle Tracking and St

TuSimple 92 Jan 03, 2023
Code Repo for the ACL21 paper "Common Sense Beyond English: Evaluating and Improving Multilingual LMs for Commonsense Reasoning"

Common Sense Beyond English: Evaluating and Improving Multilingual LMs for Commonsense Reasoning This is the Github repository of our paper, "Common S

INK Lab @ USC 19 Nov 30, 2022
Code for the tech report Toward Training at ImageNet Scale with Differential Privacy

Differentially private Imagenet training Code for the tech report Toward Training at ImageNet Scale with Differential Privacy by Alexey Kurakin, Steve

Google Research 29 Nov 03, 2022
Code for "Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification", ECCV 2020 Spotlight

Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification Implementation of "Learning From Multiple Experts: Se

27 Nov 05, 2022
A research toolkit for particle swarm optimization in Python

PySwarms is an extensible research toolkit for particle swarm optimization (PSO) in Python. It is intended for swarm intelligence researchers, practit

Lj Miranda 1k Dec 30, 2022
PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Sharpness-aware Quantization for Deep Neural Networks This is the official repository for our paper: Sharpness-aware Quantization for Deep Neural Netw

Zhuang AI Group 30 Dec 19, 2022
TensorFlow implementation of Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently.

Adversarial Chess TensorFlow implementation of Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently. Requirements To run

Muthu Chidambaram 30 Sep 07, 2021
meProp: Sparsified Back Propagation for Accelerated Deep Learning (ICML 2017)

meProp The codes were used for the paper meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting (ICML 2017) [pdf]

LancoPKU 107 Nov 18, 2022
PyElecCL - Electron Monte Carlo Second Checks

PyElecCL Python program to perform second checks for electron Monte Carlo radiat

Reese Haywood 3 Feb 22, 2022
"3D Human Texture Estimation from a Single Image with Transformers", ICCV 2021

Texformer: 3D Human Texture Estimation from a Single Image with Transformers This is the official implementation of "3D Human Texture Estimation from

XiangyuXu 193 Dec 05, 2022
Pytorch implementation of “Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement”

Graph-to-Graph Transformers Self-attention models, such as Transformer, have been hugely successful in a wide range of natural language processing (NL

Idiap Research Institute 40 Aug 14, 2022
A flexible submap-based framework towards spatio-temporally consistent volumetric mapping and scene understanding.

Panoptic Mapping This package contains panoptic_mapping, a general framework for semantic volumetric mapping. We provide, among other, a submap-based

ETHZ ASL 194 Dec 20, 2022
Generates all variables from your .tf files into a variables.tf file.

tfvg Generates all variables from your .tf files into a variables.tf file. It searches for every var.variable_name in your .tf files and generates a v

1 Dec 01, 2022
PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models

Deepvoice3_pytorch PyTorch implementation of convolutional networks-based text-to-speech synthesis models: arXiv:1710.07654: Deep Voice 3: Scaling Tex

Ryuichi Yamamoto 1.8k Jan 08, 2023
Adaptive Attention Span for Reinforcement Learning

Adaptive Transformers in RL Official implementation of Adaptive Transformers in RL In this work we replicate several results from Stabilizing Transfor

100 Nov 15, 2022
SLAMP: Stochastic Latent Appearance and Motion Prediction

SLAMP: Stochastic Latent Appearance and Motion Prediction Official implementation of the paper SLAMP: Stochastic Latent Appearance and Motion Predicti

Kaan Akan 34 Dec 08, 2022
A novel benchmark dataset for Monocular Layout prediction

AutoLay AutoLay: Benchmarking Monocular Layout Estimation Kaustubh Mani, N. Sai Shankar, J. Krishna Murthy, and K. Madhava Krishna Abstract In this pa

Kaustubh Mani 39 Apr 26, 2022
gym-anm is a framework for designing reinforcement learning (RL) environments that model Active Network Management (ANM) tasks in electricity distribution networks.

gym-anm is a framework for designing reinforcement learning (RL) environments that model Active Network Management (ANM) tasks in electricity distribution networks. It is built on top of the OpenAI G

Robin Henry 99 Dec 12, 2022
This is the code for HOI Transformer

HOI Transformer Code for CVPR 2021 accepted paper End-to-End Human Object Interaction Detection with HOI Transformer. Reproduction We recomend you to

BigBangEpoch 124 Dec 29, 2022