Make a surveillance camera from your raspberry pi!

Overview

rpi-surveillance

Make a surveillance camera from your Raspberry Pi 4!

The surveillance is built as following: the camera records 10 seconds video and if a motion was detected - sends the video to telegram channel.

The timestamp is printed on videos, so it is better to set the correct time on your Raspberry Pi.

The motion detection works in the following way: the camera’s H.264 encoder calculates motion vector estimates while generating compressed video. Using these vectors we threshold them by --magnitude-th argument. If more than --vectors-quorum vectors thresholded - mark current frame as containing motion. If there are more than --detection-frames consecutive frames with motion - motion detected.

Tested on Raspberry Pi 4 (4 RAM) + NoIR Camera V2.

Installation

Install package

Install Python 3 requirements:

pip3 install --user -r requirements.txt

Install provided .deb package:

sudo dpkg -i <path/to/downloaded/rpi-surveillance.deb>
sudo apt install -f

Note: the installation supposes that you already enabled camera module on your Raspberry Pi.

Create telegram bot and chat

  1. Write to @BotFather in telegram and create a bot:
/start
/newbot
<name of your bot>
<username of your bot>_bot

You will get the TOKEN. Save it for future use.

  1. Create a private channel where you will receive video sequences with motion.
  2. Add created bot to the channel (rerquires only "post messages" permission).
  3. Send message test to the channel.
  4. Run /usr/lib/rpi-surveillance/get_channel_id to get the CHANNEL_ID. Save it for future use.

Usage

To launch surveillance just run rpi-surveillance with your TOKEN and CHANNEL_ID, for example:

rpi-surveillance --token 1259140266:WAaqkMycra87ECzRZwa6Z_8T9KB4N-8OPI --channel-id -1003209177928

You can set various parameters of the surveillance:

usage: rpi-surveillance [-h] [--config CONFIG] --token TOKEN --channel-id
                        CHANNEL_ID [--temp-dir TEMP_DIR] [--log-file LOG_FILE]
                        [--resolution {640x480,1280x720,1920x1080}]
                        [--fps {25,30,60}] [--rotation {0,90,180,270}]
                        [--duration DURATION] [--magnitude-th MAGNITUDE_TH]
                        [--vectors-quorum VECTORS_QUORUM]
                        [--detection-frames DETECTION_FRAMES]

optional arguments:
  -h, --help            show this help message and exit
  --config CONFIG       Path to config file.
  --token TOKEN         Token for your telegram bot.
  --channel-id CHANNEL_ID
                        Telegram channel ID. If you don't have it please, send
                        a message to your channel and run /usr/lib/rpi-
                        surveillance/get_channel_id with your token.
  --temp-dir TEMP_DIR   Path to temporary directory for video saving before
                        sending to channel. Don't change it if you don't know
                        what you're doing.
  --log-file LOG_FILE   Path to log file for logging.
  --resolution {640x480,1280x720,1920x1080}
                        Camera resolution. Default - 640x480.
  --fps {25,30,60}      Frames per second. Default - 25.
  --rotation {0,90,180,270}
                        Frame rotation. Default - 0.
  --duration DURATION   Duration of videos in seconds. Default - 10.
  --magnitude-th MAGNITUDE_TH
                        Magnitude threshold for motion detection (lower - more
                        sensitive). Defaults: for 640x480 - 15, for 1280x720 -
                        40, for 1920x1080 - 65.
  --vectors-quorum VECTORS_QUORUM
                        Vectors quorum for motion detection (lower - more
                        sensitive). Defaults: for 640x480 - 10, for 1280x720 -
                        20, for 1920x1080 - 40.
  --detection-frames DETECTION_FRAMES
                        The number of consecutive frames with detected motion
                        to send an alert.

Build

Build was done using dpkg-buildpackage.

You might also like...
Make your master artistic punk avatar through machine learning world famous paintings.
Make your master artistic punk avatar through machine learning world famous paintings.

Master-art-punk Make your master artistic punk avatar through machine learning world famous paintings. 通过机器学习世界名画制作属于你的大师级艺术朋克头像 Nowadays, NFT is beco

Python-experiments - A Repository which contains python scripts to automate things and make your life easier with python
Python-experiments - A Repository which contains python scripts to automate things and make your life easier with python

Python Experiments A Repository which contains python scripts to automate things

A very lightweight monitoring system for Raspberry Pi clusters running Kubernetes.
A very lightweight monitoring system for Raspberry Pi clusters running Kubernetes.

OMNI A very lightweight monitoring system for Raspberry Pi clusters running Kubernetes. Why? When I finished my Kubernetes cluster using a few Raspber

Run object detection model on the Raspberry Pi

Using TensorFlow Lite with Python is great for embedded devices based on Linux, such as Raspberry Pi.

 Tutorial to set up TensorFlow Object Detection API on the Raspberry Pi
Tutorial to set up TensorFlow Object Detection API on the Raspberry Pi

A tutorial showing how to set up TensorFlow's Object Detection API on the Raspberry Pi

An air quality monitoring service with a Raspberry Pi and a SDS011 sensor.

Raspberry Pi Air Quality Monitor A simple air quality monitoring service for the Raspberry Pi. Installation Clone the repository and run the following

A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!
A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~
🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~

YOLOv5-Lite:lighter, faster and easier to deploy Perform a series of ablation experiments on yolov5 to make it lighter (smaller Flops, lower memory, a

A facial recognition doorbell system using a Raspberry Pi

Facial Recognition Doorbell This project expands on the person-detecting doorbell system to allow it to identify faces, and announce names accordingly

Releases(v2.2.2)
Owner
Vladyslav
Machine learning and computer vision developer.
Vladyslav
Applying CLIP to Point Cloud Recognition.

PointCLIP: Point Cloud Understanding by CLIP This repository is an official implementation of the paper 'PointCLIP: Point Cloud Understanding by CLIP'

Renrui Zhang 175 Dec 24, 2022
HybridNets: End-to-End Perception Network

HybridNets: End2End Perception Network HybridNets Network Architecture. HybridNets: End-to-End Perception Network by Dat Vu, Bao Ngo, Hung Phan 📧 FPT

Thanh Dat Vu 370 Dec 29, 2022
Time Series Forecasting with Temporal Fusion Transformer in Pytorch

Forecasting with the Temporal Fusion Transformer Multi-horizon forecasting often contains a complex mix of inputs – including static (i.e. time-invari

Nicolás Fornasari 6 Jan 24, 2022
Source code of CIKM2021 Long Paper "PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling".

PSSL Source code of CIKM2021 Long Paper "PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling". It consists of the pre-tra

2 Dec 21, 2021
Fast, accurate and reliable software for algebraic CT reconstruction

KCT CBCT Fast, accurate and reliable software for algebraic CT reconstruction. This set of software tools includes OpenCL implementation of modern CT

Vojtěch Kulvait 4 Dec 14, 2022
Pytorch implementation of the paper "COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking."

Expert-Linking Pytorch implementation of the paper "COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking." This is

BoChen 12 Jan 01, 2023
Intro-to-dl - Resources for "Introduction to Deep Learning" course.

Introduction to Deep Learning course resources https://www.coursera.org/learn/intro-to-deep-learning Running on Google Colab (tested for all weeks) Go

Advanced Machine Learning specialisation by HSE 761 Dec 24, 2022
AOT-GAN for High-Resolution Image Inpainting (codebase for image inpainting)

AOT-GAN for High-Resolution Image Inpainting Arxiv Paper | AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image Inpainting Yanhong

Multimedia Research 214 Jan 03, 2023
Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition (AGRA, ACM 2020, Oral)

Cross Domain Facial Expression Recognition Benchmark Implementation of papers: Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchm

89 Dec 09, 2022
This repo is about to create the Streamlit application for given ML model.

HR-Attritiion-using-Streamlit This repo is about to create the Streamlit application for given ML model. Problem Statement: Managing peoples at workpl

Pavan Giri 0 Dec 10, 2021
Code for the paper "Adapting Monolingual Models: Data can be Scarce when Language Similarity is High"

Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling Adapting Monolingual Models: Data can be Scarce when Language Similarity is High

Wietse de Vries 5 Aug 02, 2021
Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020)

Causality In Traffic Accident (Under Construction) Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020) Overview Data Prepa

Tackgeun 21 Nov 20, 2022
Robust and Accurate Object Detection via Self-Knowledge Distillation

Robust and Accurate Object Detection via Self-Knowledge Distillation paper:https://arxiv.org/abs/2111.07239 Environments Python 3.7 Cuda 10.1 Prepare

Weipeng Xu 6 Jul 01, 2022
Explaining in Style: Training a GAN to explain a classifier in StyleSpace

Explaining in Style: Official TensorFlow Colab Explaining in Style: Training a GAN to explain a classifier in StyleSpace Oran Lang, Yossi Gandelsman,

Google 197 Nov 08, 2022
Evaluation Pipeline for our ECCV2020: Journey Towards Tiny Perceptual Super-Resolution.

Journey Towards Tiny Perceptual Super-Resolution Test code for our ECCV2020 paper: https://arxiv.org/abs/2007.04356 Our x4 upscaling pre-trained model

Royson 6 Mar 30, 2022
A public available dataset for road boundary detection in aerial images

Topo-boundary This is the official github repo of paper Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images

Zhenhua Xu 79 Jan 04, 2023
Chinese Mandarin tts text-to-speech 中文 (普通话) 语音 合成 , by fastspeech 2 , implemented in pytorch, using waveglow as vocoder,

Chinese mandarin text to speech based on Fastspeech2 and Unet This is a modification and adpation of fastspeech2 to mandrin(普通话). Many modifications t

291 Jan 02, 2023
Code to reproduce the results for Compositional Attention

Compositional-Attention This repository contains the official implementation for the paper Compositional Attention: Disentangling Search and Retrieval

Sarthak Mittal 58 Nov 30, 2022
Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution

FAU Implementation of the paper: Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution. Yingruo

Evelyn 78 Nov 29, 2022
CPU inference engine that delivers unprecedented performance for sparse models

The DeepSparse Engine is a CPU runtime that delivers unprecedented performance by taking advantage of natural sparsity within neural networks to reduce compute required as well as accelerate memory b

Neural Magic 1.2k Jan 09, 2023