Urban Environment Simulator Code for Testing your Target Tracking Algorithms.
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Updated
Feb 5, 2021 - Python
Urban Environment Simulator Code for Testing your Target Tracking Algorithms.
The Gaussian Mixture Probability Hypothesis Density filter, GMPHD python implementation
Python implementation of CLEAR multi object tracking (MOT) evaluation metrics
Real-time target tracking for DJI-Tello using HSV color masking
Probabilistic Multiple Hypothesis Tracking
GM-PHD filter implementation in python (Gaussian mixture probability hypothesis density filter)
This Black Python script is not a game! It is a powerful tool to monitor the traffic between clients and malicious .onion sites. We use a code like this to capture bad actors
Application allows for ifnormation entropy analysis of 2D image data. Analysis outcome can be stored in No-SQL database and then recovered and plotted for better understanding of underlying data
A target tracking toolbox developed in Python. It aims to demonstrate how target tracking works and to serve as a testing environment for target tracking problems.
A tracking scheme developed by integrating six tracking methods, DeepSORT StrongSORT OSNet HybridSORT, OCSORT, and ByteTrack, using yolov5
A camera that follows a person based on their face characteristics.
Target tracking using Kalman filter prediction - OpenCV
This is a Python app I created for the DJI Tello to allow it to follow a user with face tracking using OpenCV!
whoisrecon is a Python command-line tool designed for WHOIS reconnaissance, providing a streamlined way to find related domains from current and historical records with wildcard supported searches of email, organization, and more!.
This project summarizes some core traditional visual algorithms commonly used in all drone competitions during undergraduate studies
Overall, this project provides a flexible framework for implementing computer vision applications with a focus on object detection, tracking, and point scoring. Its versatility and modularity make it suitable for a wide range of use cases across different industries and domains.
Sem4 LAAS-CNRS Internship: Time Estimation for Occlusion Avoidance and Tracking by Deep Learning (YOLOv3, YOLOv4 & Detectron2)
A motion object tracking application created using OpenCV. After running the application, select the object you want to track.
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