There are 11 repositories under video-surveillance topic.
Open-Source AI Camera. Empower any camera/CCTV with state-of-the-art AI, including facial recognition, person recognition(RE-ID) car detection, fall detection and more
An open and scalable video surveillance system for anyone making this world a better and more peaceful place.
(DEPRECATED) An open source image processing framework, which uses your USB-, IP- or RPi-camera to recognize events (e.g. motion).
(DEPRECATED) An open source GUI to configure the machinery and to view events that were detected by the machinery.
A Linux OS created by Buildroot which runs Kerberos Open Source out-of-the-box.
FgSegNet_v2: "Learning Multi-scale Features for Foreground Segmentation.” by Long Ang LIM and Hacer YALIM KELES
Deep learning based object tracking with line crossing and area intrusion detection
Run Kerberos Open Source inside a docker container.
A video monitoring client based on Qt and FFmpeg.(基于Qt+FFmpeg的视频监控软件)
Distributed Motion Surveillance Security System (DMS3): a Go-based distributed video security system
Connect all CCTV cameras to this system to track someone's live location in a premise using facial recognition. It can be also used to maintain records of people entering a premise using their face instead of bio-metrics/cards/manual Entry
A scalable single pane of glass for your ever growing surveillance landscape with dashboards, analytics, notifications, device management, sites and grouping.
An enterprise ready, resilient and horizontal scalable solution for large video landscapes.
A smart surveillance system using CCTV Surveillance camera in order to detect hammers, weapons involved in a break in. This Surveillance system focuses on automation of Video Surveillance in ATM machines and detect any type of potential criminal activities. Technologies used open CV, Computer Vision
Unsupervised deep learning system for local anomaly event detection in crowded scenes
Because of the COVID-19 pandemic of 2020, more and more people are concerned with protecting themselves using masks, thus the need of software capable of monitoring whether the people are wearing masks or not. That is why I created a Python application using OpenCV (with CUDA support) based on the YOLOv4 algorithm, capable of monitoring the safety level of a space with video surveillance.
Lightweight Video Surveillance System w/ ffmpeg, motion, and dual-stream cameras for use on Raspberry Pi
Video surveillance using deep learning models
Decomposition into Low-Rank and Sparse Matrices in Computer Vision
Because of the COVID-19 pandemic of 2020, more and more people are concerned with protecting themselves using masks, thus the need of software capable of monitoring whether the people are wearing masks or not. That is why I created a Python application using OpenCV (with CUDA support) based on the YOLOv4 algorithm, more precisely the tiny option, capable of monitoring the safety level of a space with video surveillance. This is the Jetson Nano version of the application.
A machine learning-based social distance estimation and crowd monitoring system for surveillance cameras
Code for our paper titled "IoT Enabled Video surveillance system using Raspberry Pi." As the title suggests it's an IoT enabled, low-cost and extremely user friendly surveillance system implemented using the Raspberry Pi.
Ruby-based video security surveillance system using Motion (a software motion detector)
REST API server to detect people in video files using TensorFlow Lite.
Contains home security/automation modules for motion detection and temperature alert (used Raspberry Pi Zero). Output trigger and module's setting could be configured from a computer software. Raspberry Pi Zero communicates through the TCP connection. The package keeps the log of motion detection and the temperature. It also has SMS alert features.
The intelligent surveillance of footages will be performed via a spatio temporal autoencoder. This will be based on a 3D convolution network. The encoder part extracts the spatial and temporal information, and then the decoder reconstructs the frames. The abnormal events are identified by computing the reconstruction loss using Euclidean distance between original and reconstructed batch.
Implementation of yoloV4 on video with opencv and python
Webcam software for video surveillance