Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans

A campus security app that uses machine vision and a trained model to detect violent and non-violent behavior. By tracking individuals with MediaPipe and using TensorFlow + LSTM, it alerts security when hostile actions are detected, enabling early intervention to prevent escalation and ensure campus safety.

PyTorch-based toolkit for landmark localization

Practical solutions for the Fundamental 3D Computer Vision (CE-344) course at Sharif University of Technology

Object Detection | Keypoint Detection

This computer vision project analyzes tennis match videos using cutting-edge techniques. It employs YOLOv8 for player detection, finetuned YOLO for ball tracking, and ResNet50 for extracting court keypoints. Additionally, it calculates player and ball speeds and generates a mini court reflecting player positions.

Predictions of the four corners of documents.

SoccerNet@CVPR | 1st place solution for Camera Calibration Challenge 2023

Cybervision can generate a 3D model from two photos of an object

computer vision and sports

This repository contains a collection of projects developed during a university Python course. Each project explores different aspects of data processing, analysis, and computer vision.

25581-Images-88-Facial-Landmarks-Annotation-Data

50356-Images-Human-Body-Segmentation-and-18-Landmarks-Data

"Learning Delicate Local Representations for Multi-Person Pose Estimation" (ECCV 2020 Spotlight) & (COCO 2019 Human Keypoint Detection Challenge Winner) & (COCO 2019 Best Paper Award)

Code for paper on ICRA 2022 workshop on Deformable Object Manipulation. In this work we learn keypoints from synthetic data for robotic cloth folding.

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

Face Morphing Java Android app.

Trained YOLOv8 model on Pose estimation with sperm cells.

The official implementation of paper: "Multi-Grained Contrast for Data-Efficient Unsupervised Representation Learning"