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https://github.com/Psynosaur/Jetson-SecVision

Person detection for Hikvision DVRs, uses TensorRT and yolov4
https://github.com/Psynosaur/Jetson-SecVision

basic-image-server darknet hikvision hikvision-camera-bot home-automation nvidia-jetson-nano object-detection telegram yolo yolov4 yolov4-object-detection

Last synced: 3 months ago
JSON representation

Person detection for Hikvision DVRs, uses TensorRT and yolov4

Host: GitHub
URL: https://github.com/Psynosaur/Jetson-SecVision
Owner: Psynosaur
License: gpl-3.0
Created: 2021-09-21T04:37:14.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-03-16T05:05:12.000Z (8 months ago)
Last Synced: 2024-06-16T14:33:54.009Z (5 months ago)
Topics: basic-image-server, darknet, hikvision, hikvision-camera-bot, home-automation, nvidia-jetson-nano, object-detection, telegram, yolo, yolov4, yolov4-object-detection
Language: Python
Homepage:
Size: 5.49 MB
Stars: 22
Watchers: 3
Forks: 3
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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awesome-yolo-object-detection - Psynosaur/Jetson-SecVision - SecVision?style=social"/> : Person detection for Hikvision DVR with AlarmIO ports, uses TensorRT and yolov4. (Applications)
awesome-cuda-and-hpc - Psynosaur/Jetson-SecVision - SecVision?style=social"/> : Person detection for Hikvision DVR with AlarmIO ports, uses TensorRT and yolov4. (Applications)
awesome-cuda-and-hpc - Psynosaur/Jetson-SecVision - SecVision?style=social"/> : Person detection for Hikvision DVR with AlarmIO ports, uses TensorRT and yolov4. (Applications)
awesome-yolo-object-detection - Psynosaur/Jetson-SecVision - SecVision?style=social"/> : Person detection for Hikvision DVR with AlarmIO ports, uses TensorRT and yolov4. (Applications)

README

## NVIDIA Jetson - SecVision

Technologies used
- Jetson Nano
- TensorRT
- Darknet - Yolov4-416
- HikVision DVR DS-7208HUHI-K2

### Prerequisites

- **PLEASE BUILD THIS PROJECT FIRST**
- https://github.com/jkjung-avt/tensorrt_demos
- Environment is ready when Demo #5 runs with yolov4-416 model
- Running redis instance
- https://redis.io/topics/quickstart
- Follow the instructions to set up a bot and get your user id
- https://www.instructables.com/Set-up-Telegram-Bot-on-Raspberry-Pi/
- Once the bot is set up send it a message on telegram
- In your browser paste the following url
- https://api.telegram.org/bot{token}/getUpdates
- Then get the id in the "from" object with your username in
- Replace both **token** and **id** in the settings.ini file

### Findings

- With a 4TB HDD, there about 720 days of video recordings, before it starts overwriting data.

## To Do

- [ ] Add surveillance center notifications
- [ ] Make Telegram notifications for all channels from settings and optional
- [ ] Determine on and off times per camera
- [ ] Upgrade dashboard, with recording stats
- [ ] Software stats, running, failing etc...
- [ ] Channel Trigger info
- [ ] Person counter
- [ ] Captured frames, garbage collection...

### Usage

Setup settings.ini

[DVR]
username = dummy => user for DVR login
password = SuchPass => password for DVR login
channels = 8 => channels on DVR
ip = 0.0.0.0 => IP address of DVR on local network
[Telegram]
token = botapitoken => get this from the telegram BotFather
id = userid => the id of your user chatting to the bot

On DVR set basic auth for HTTP request

git clone https://github.com/Psynosaur/JetsonSecVision && cd JetsonSecVision
pip3 install aiofiles aiohttp==3.7.4 asyncio colorlog redis

### Project needs symlinks to tensorrt_demo project
#### Setup environment

cd detect_yolo
ln -s ${HOME}/tensorrt demos/utils/ ./utils
ln -s ${HOME}/tensorrt_demos/plugins/ ./plugins
ln -s ${HOME}/tensorrt_demos/yolo/ ./yolo
cd ..

#### Run script with model of your choice built from the TensorRT demos instructions

python3 detect_yolo/detect_yolo.py -m yolov4-416

Takes approximately 2.4 seconds round trip to do its thing for 8x2MP images and is very accurate
Detection step takes 1.68s for a network fps of **~4.57FPS**.

### When the jetson is overclocked this is at around ~5.7FPS at 1.15GHz GPU and consumes ~14.5W at the wall.

This routine is not very resource intensive as a video feed would be, nor would it be as unstable at these speeds.

![jtop](./detect_yolo/jtop.png)

here it is doing its thing

![jtop](./detect_yolo/nano.png)

Channel and zone AlarmIO wiring

![wiring](./detect_yolo/AlarmIO.jpg)

### Automatic / Continuous Operation

### Installation

$ sudo detect_yolo/install_yolo.sh

### Check status of service

$ sudo service detect_yolo status

### To stop the service, simply run:

$ sudo service detect_yolo stop

### To uninstall the service

$ sudo detect_yolo/uninstall_yolo.sh

### Developers

$ sudo detect_yolo/refresh_yolo.sh

### Expected output

![Detection](./detect_yolo/img.jpg)

Dec 04 13:59:57 jetson secvision_yolo[19702]: INFO: GET DATA - 0.69s
Dec 04 13:59:57 jetson secvision_yolo[19702]: WARNING: 201 started recording
Dec 04 13:59:57 jetson secvision_yolo[19702]: WARNING: Front door - 0.88 - 1 person found in zone 1 - start recording
Dec 04 13:59:57 jetson secvision_yolo[19702]: DEBUG:Using selector: EpollSelector
Dec 04 13:59:58 jetson secvision_yolo[19702]: INFO: Main loop - 2.16s Inference loop - 1.47s @ 5.46fps
Dec 04 13:59:59 jetson secvision_yolo[19702]: WARNING: Front door person found 1.288873s ago
Dec 04 13:59:59 jetson secvision_yolo[19702]: WARNING: Sent telegram message via API and it took 1.348s
Dec 04 13:59:59 jetson secvision_yolo[19702]: INFO: GET DATA - 0.70s
Dec 04 13:59:59 jetson secvision_yolo[19702]: WARNING: Front door - 1.00 - 1 person found in zone 1 - recording
Dec 04 13:59:59 jetson secvision_yolo[19702]: INFO:201 event exists
Dec 04 14:00:01 jetson secvision_yolo[19702]: INFO: Main loop - 2.12s Inference loop - 1.42s @ 5.63fps
Dec 04 14:00:01 jetson secvision_yolo[19702]: INFO: GET DATA - 0.76s
Dec 04 14:00:03 jetson secvision_yolo[19702]: INFO: Main loop - 2.18s Inference loop - 1.42s @ 5.63fps
Dec 04 14:00:03 jetson secvision_yolo[19702]: INFO: GET DATA - 0.72s
Dec 04 14:00:04 jetson secvision_yolo[19702]: WARNING: Front door person found 4.141477s ago
Dec 04 14:00:05 jetson secvision_yolo[19702]: INFO: Main loop - 2.13s Inference loop - 1.41s @ 5.66fps
Dec 04 14:00:06 jetson secvision_yolo[19702]: INFO: GET DATA - 0.75s
Dec 04 14:00:07 jetson secvision_yolo[19702]: INFO: Main loop - 2.17s Inference loop - 1.42s @ 5.64fps
Dec 04 14:00:08 jetson secvision_yolo[19702]: INFO: GET DATA - 0.74s
Dec 04 14:00:09 jetson secvision_yolo[19702]: INFO: Main loop - 2.18s Inference loop - 1.44s @ 5.57fps
Dec 04 14:00:09 jetson secvision_yolo[19702]: INFO: CPU 40.00°C / GPU 36.50°C / PLL 33.50°C
Dec 04 14:00:09 jetson secvision_yolo[19702]: INFO: AO 45.00°C / THERM 38.00°C / FAN 1625.0RPM
Dec 04 14:00:09 jetson secvision_yolo[19702]: INFO: NETWORK 5.63 FPS
Dec 04 14:00:09 jetson secvision_yolo[19702]: WARNING: Front door person found 9.743311s ago
Dec 04 14:00:10 jetson secvision_yolo[19702]: INFO: GET DATA - 0.72s
Dec 04 14:00:11 jetson secvision_yolo[19702]: INFO: Main loop - 2.14s Inference loop - 1.42s @ 5.64fps
Dec 04 14:00:12 jetson secvision_yolo[19702]: INFO: GET DATA - 0.73s
Dec 04 14:00:13 jetson secvision_yolo[19702]: INFO: Main loop - 2.14s Inference loop - 1.41s @ 5.67fps
Dec 04 14:00:14 jetson secvision_yolo[19702]: WARNING: Front door person found 14.743941s ago
Dec 04 14:00:14 jetson secvision_yolo[19702]: INFO: GET DATA - 0.76s
Dec 04 14:00:16 jetson secvision_yolo[19702]: INFO: Main loop - 2.17s Inference loop - 1.41s @ 5.66fps
Dec 04 14:00:16 jetson secvision_yolo[19702]: INFO: GET DATA - 0.67s
Dec 04 14:00:18 jetson secvision_yolo[19702]: INFO: Main loop - 2.09s Inference loop - 1.42s @ 5.64fps
Dec 04 14:00:18 jetson secvision_yolo[19702]: INFO: GET DATA - 0.71s
Dec 04 14:00:19 jetson secvision_yolo[19702]: WARNING: Front door person found 19.744398s ago
Dec 04 14:00:20 jetson secvision_yolo[19702]: INFO: Main loop - 2.13s Inference loop - 1.41s @ 5.66fps
Dec 04 14:00:21 jetson secvision_yolo[19702]: INFO: GET DATA - 0.74s
Dec 04 14:00:22 jetson secvision_yolo[19702]: INFO: Main loop - 2.15s Inference loop - 1.41s @ 5.68fps
Dec 04 14:00:23 jetson secvision_yolo[19702]: INFO: GET DATA - 0.70s
Dec 04 14:00:24 jetson secvision_yolo[19702]: INFO: Main loop - 2.11s Inference loop - 1.41s @ 5.67fps
Dec 04 14:00:24 jetson secvision_yolo[19702]: WARNING: Front door person found 24.744972s ago
Dec 04 14:00:25 jetson secvision_yolo[19702]: INFO: GET DATA - 0.79s
Dec 04 14:00:26 jetson secvision_yolo[19702]: WARNING: 201 stopped recording
Dec 04 14:00:26 jetson secvision_yolo[19702]: INFO: Main loop - 2.22s Inference loop - 1.41s @ 5.68fps
Dec 04 14:00:27 jetson secvision_yolo[19702]: INFO: GET DATA - 0.76s
Dec 04 14:00:29 jetson secvision_yolo[19702]: INFO: Main loop - 2.18s Inference loop - 1.42s @ 5.63fps
Dec 04 14:00:29 jetson secvision_yolo[19702]: INFO: GET DATA - 0.73s

Dashboard available on jetson IP address on port **8080**

![jtop](./detect_yolo/mobile_dash.jpg)