Object detection for video surveillance
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Updated
Jun 6, 2024 - Python
Object detection for video surveillance
set of helper tools for the assembly of the different elements in the RELECOV platform (Spanish Network for genomic surveillance of SARS-Cov-2) as data download, processing, validation and upload to public databases, as well as analysis runs and database storage.
Paradox Magellan, Spectra and EVO, with MQTT, Signal, Pushbullet, Pushover and others
Self-hosted, local only NVR and AI Computer Vision software. With features such as object detection, motion detection, face recognition and more, it gives you the power to keep an eye on your home, office or any other place you want to monitor.
Raspberry Pi surveillance
Отправка видео с предзаписью по детектору движения Synology Surveillance Station в Telegram используя Webhook
Catframes is a script that concatenates frames with different aspect ratios. It uses FFmpeg and Pillow.
A CLI script to save messages deleted from a Telegram chat
Security Camera on a Raspberry Pi - First Year Project at INSA CVL
A driving assistant designed to detect and mitigate potential threats
Security Camera
Security camera based on a raspberry pi and Telegram, controllable by smartphone
ViGi: Camera surveillance system with motion detection and object recognition for home security.
Epidemiological weeks calculation based on the US CDC (MMWR) and ISO week numbering systems
AI-Augmenteed Surveillance System (2A2S): A CCTV system augmented with motion detection and object detection models with email alert feature
Python 3.7+ API for Logi Circle cameras
A mobile all-in-one solution for physical recon.
This is a Minor Project undertaken for the fulfillment of a Bachelors Degree in Computer Engineering where People can upload videos and find the speed of vehicles and track them
This repository contains data and code for our analysis of the genome of a Plasmodium falciparum strain with reduced susceptibility to Artemisinin-Based Combination Therapy (ACT) drugs. We performed whole-genome sequencing of the HSOG3 clinical isolate and compared it with a large dataset of global P. falciparum samples.
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