dhruvsheth-ai Goto Github PK

pedestrian-detection

Pedestrian detection using the TensorFlow Object Detection API. Includes multi GPU parallel processing inference.

pedestron

[Pedestron] Pedestrian Detection: The Elephant In The Room. On ArXiv 2020

person-autonomous-intercom

TinyML Computer Vision based smart autonomous intercom/doorbell system

person-queuing-system-arduino33

This queuing system is designed on Tensorflow TinyML framework of person detetction

phenocv-weedcam

pigar

:coffee: A fantastic tool to generate requirements.txt for your Python project, and more than that.

pinto0309

plant-growth-estimation-edgeimpulse

plantgan

plantvillage-dataset

Dataset of diseased plant leaf images and corresponding labels

playground

Play with neural networks!

point-cloud-prediction

Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks

progressive_growing_of_gans

Progressive Growing of GANs for Improved Quality, Stability, and Variation

rexdivakar

rigid-dynamics-gelare

Repository for exploring simulations and rigid dynamical systems to model constraints in Gelare.

robmarkcole

satellite-image-classification

satellite-image-deep-learning

Resources for deep learning with satellite & aerial imagery

self-sanitisation-person-detetction

This project has been designed for Arduino touch free-challenge Functioning: Usually, in malls or supermarkets, many people touch certain objects in aisles and it is really hard to monitor each object and sanitise each aisle individually. Most of the times, manual sanitisation is completed after each day and individual sanitisation of each aisle is not possible. Sometimes it happens that the food-aisle is more contaminated and the clothes-aisle is less contaminated, hence extra care is required for monitoring and sanitisatio of food-aisle. But such a monitoring solution is not possible manually. Therefore, I decided to make an autonomous solution based on TinyML deployed on Arduino Nano 33 BLE Sense. The person-identification model is highly accurate and uses an arducam which can be used in 5mp or 2mp variants. In solutions that contain ultrasonic sensors, or Lidar sensors, the readings or measurements are not accurate and can send false readings. Hence, the person detction model uses person recogition technology based on TensorflowLite framework. When, 1-50 people are detcted, a green light is switched on which means that the area is yet secure. When, 51-75 people are detected, a yellow light is flashed which means that awareness is required. When, 76-100 people are detected, a red light is flashed which means that people need to carefully touch objects and only touch those objects which are required. When 101-110 people are detected, the UV sanitisation system is activated which sanitises the complete area and objects are secure again. The count is again reset to 0 and the cycle is looped. The people vs Time graph is potrayed on the ThingSpeak web app where mall owners can track the time when the crowd is more or less and accordingly maintain more awareness during peak timings.

sensor-data-b-l475e-iot01a

Data capture from sensors on B-L475E-IOT01A ST IoT Discovery kit

sentiment-analysis

Analysis with Streamlit

sentiment-analysis-streamlit

sfmlearner-pytorch

Pytorch version of SfmLearner from Tinghui Zhou et al.

shape-angle-and-distance-estimation

sociopulse

spatio-temporal-gan

spike-prime

Experiments with the LEGO Mindstorms (51515) and SPIKE Prime (45678)

spot-challenge-wildfires

http://ibm.biz/cfcsc-wildfires - predict the wildfire area for 7 regions in Australia for each day in February 2021

spot-wildfire-challenge

streamlit

colab