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  • 👋 Hi, I’m @pereldegla
  • 👀 I’m interested in computer vision, machine learning and robotics
  • 🌱 I’m currently learning computer vision algorithms
  • 📫 How to reach me : [email protected]

pereldegla's Projects

airflow icon airflow

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

autonomous-turtlebot icon autonomous-turtlebot

Methods and 3d models for autonomous navigation of Turtlebot3 Burger through simulted and real world. End of study project with a +10 team. More details coming soon.

basketballpredictor icon basketballpredictor

A basket ball shot predictor. It will first track the ball using its contours and based on its initial travelling points, predict whether the shot will land in the basket.

facemask-detection icon facemask-detection

Face detection with MTCNN/Haar cascades and image classification using a pre-trained ResNet

facemask-detection-using-lobe-no-code icon facemask-detection-using-lobe-no-code

The model used was generated using Lobe no code image classification. This can be paired with a face detection algorithm then classifying multiple faces in the image.

fallen-person-detection-using-yolov5-and-svm icon fallen-person-detection-using-yolov5-and-svm

Detecting falls can be helpful in retirement homes. I've started this project while working for KOMPAÏ ROBOTICS. They make robotics companions for the elders. I will be adding some improvements and explanations soon.

recommender-systems-with-python icon recommender-systems-with-python

A basic recommendation system by suggesting items that are most similar to a particular item, in this case, movies. This is not a true robust recommendation system, to describe it more accurately,it just tells you what movies/items are most similar to your movie choice.

semantic-segmentation-for-drone-images icon semantic-segmentation-for-drone-images

Aerial semantic segmentation using urban scenes for increasing the safety of autonomous drone flight and landing procedures. The imagery depicts more than 20 houses from nadir (bird's eye) view acquired at an altitude of 5 to 30 meters above ground. A high resolution camera was used to acquire images at a size of 6000x4000px (24Mpx). The training set contains 400 publicly available images and the test set is made up of 200 private images.

yoga_assistant icon yoga_assistant

We use the Blaze pose for detecting human poses and classify yoga poses

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