The Intelligent Queue Management kit is designed to help businesses manage customer queues more effectively by analyzing camera video streams and detecting the number of people in each queue. By leveraging this data, the system optimizes the queuing process to reduce customer wait times. The kit ensures efficient performance and seamless integration by utilizing OpenVINO™, a toolkit that enables developers to deploy deep learning models across various hardware platforms.
This kit uses the following technology stack:
Check out our AI Reference Kits repository for other kits.
New updates will be added here.
Table of Contents
Now, let's dive into the steps starting with installing Python. We recommend using Ubuntu to set up and run this project.
Star the repository (optional, but recommended :))
This project requires Python 3.9 or higher and a few libraries. If you don't have Python installed on your machine, go to https://www.python.org/downloads/ and download the latest version for your operating system. Follow the prompts to install Python, making sure to check the option to add Python to your PATH environment variable.
Install libraries and tools:
sudo apt install git git-lfs gcc python3-venv python3-devNOTE: If you are using Windows, you will probably need to install Microsoft Visual C++ Redistributable also.
To clone the repository, run the following command:
git clone https://github.com/openvinotoolkit/openvino_build_deploy.gitThe above will clone the repository into a directory named "openvino_build_deploy" in the current directory. Then, navigate into the directory using the following command:
cd openvino_build_deploy/ai_ref_kits/intelligent_queue_managementThen pull the video sample:
git lfs -X= -I=data/ pullTo create a virtual environment, open your terminal or command prompt and navigate to the directory where you want to create the environment. Then, run the following command:
python3 -m venv venvThis will create a new virtual environment named "venv" in the current directory.
Activate the virtual environment using the following command:
source venv/bin/activate # For Unix-based operating systems such as Linux or macOSNOTE: If you are using Windows, use venv\Scripts\activate command instead.
This will activate the virtual environment and change your shell's prompt to indicate that you are now working within that environment.
To install the required packages, run the following commands:
python -m pip install --upgrade pip
pip install -r requirements.txtTo convert and optimize the YOLOv8 model, run the following command:
python convert_and_optimize.py --model_name yolov8m --model_dir model --data_dir data --quantize TrueThis will convert the YOLOv8 model to an OpenVINO™ Intermediate Representation (IR) format and optimize it for use with OpenVINO™. You can run the Python script or check out convert-and-optimize-the-model.ipynb to learn more.
To run the application, use the following command:
python app.py --stream data/sample_video.mp4 --model_path model/yolov8m_openvino_int8_model/yolov8m.xml --zones_config_file config/zones.json --customers_limit 3This will run the application with the specified arguments. Replace "video_file.mp4" with the path to your input video file, "zones.json" with the path to your zones configuration file, and "3" with the maximum number of customers allowed in the queue. You can also run the run-the-application.ipynb to learn more about the inference process. To stop the application at any time, press 'q' or escape.
NOTE: Alternatively, you can run all steps with the following command:
python main.py --stream data/sample_video.mp4Benchmarking provides insight into your model's real-world performance. Performance may vary based on use and configuration.
Benchmarking was performed on an Intel® Xeon® Platinum 8480+ (1 socket, 56 cores) running Ubuntu 22.04.2 LTS. The tests utilized the YOLOv8m model with OpenVINO 2023.0. For complete configuration, please check the Appendix section.
Use the following command to run the benchmark:
!benchmark_app -m $int8_model_det_path -d $device -hint latency -t 30Replace int8_model_det_path with the path to your INT8 model and $device with the specific device you're using (CPU, GPU, etc.). This command performs inference on the model for 30 seconds. Run benchmark_app --help for additional command-line options.
Congratulations! You have successfully set up and run the Intelligent Queue Management application with OpenVINO™.
Platform Configurations for Performance Benchmarks for YOLOv8m Model
| Type Device | CPU | GPU | ||||
|---|---|---|---|---|---|---|
| System Board | Intel Corporation D50DNP1SBB |
AAEON UPN-ADLN01 V1.0 220950173 |
Intel® Client Systems NUC12SNKi72 |
Intel Corporation M50CYP2SBSTD |
Intel® Client Systems NUC12SNKi72 |
Intel® Client Systems NUC12SNKi72 |
| CPU | Intel(R) Xeon(R) Platinum 8480+ |
Intel® Core™ i3-N305 @ 3.80 GHz |
12th Gen Intel® Core™ i7-12700H @ 2.30 GHz |
Intel(R) Xeon(R) Gold 6348 CPU @ 2.60GHz |
12th Gen Intel® Core™ i7-12700H @ 2.30 GHz |
12th Gen Intel® Core™ i7-12700H @ 2.30 GHz |
| Sockets / Physical cores | 1 / 56 (112 Threads) |
1 / 8 (8 Threads) |
1 /14 (20 Threads) |
2 / 28 (56 Threads) |
1 /14 (20 Threads) |
1 /14 (20 Threads) |
| HyperThreading / Turbo Setting | Enabled / On | Disabled | Enabled / On | Enabled / On | Enabled / On | Enabled / On |
| Memory | 512 GB DDR4 @ 4800 MHz |
16GB DDR5 @4800 MHz |
64 GB DDR4 @ 3200 MHz |
256 GB DDR4 @ 3200 MHz |
64 GB DDR4 @ 3200 MHz |
64 GB DDR4 @ 3200 MHz |
| OS | Ubuntu 22.04.2 LTS | Ubuntu 22.04.2 LTS | Windows 11 Enterprise v22H2 |
Ubuntu 22.04.2 LTS | Windows 11 Enterprise v22H2 |
Windows 11 Enterprise v22H2 |
| Kernel | 5.15.0-72-generic | 5.15.0-1028-intel-iotg | 22621.1702 | 5.15.0-57-generic | 22621.1702 | 22621.1702 |
| Software | OpenVINO 2023.0 | OpenVINO 2023.0 | OpenVINO 2023.0 | OpenVINO 2023.0 | OpenVINO 2023.0 | OpenVINO 2023.0 |
| BIOS | Intel Corp. SE5C7411.86B.9525 .D13.2302071333 |
American Megatrends International, LLC. UNADAM10 |
Intel Corp. SNADL357.0053 .2022.1102.1218 |
Intel Corp. SE5C620.86B.01 .01.0007.2210270543 |
Intel Corp. SNADL357.0053 .2022.1102.1218 |
Intel Corp. SNADL357.0053 .2022.1102.1218 |
| BIOS Release Date | 02/07/2023 | 12/15/2022 | 11/02/2022 | 10/27/2022 | 11/02/2022 | 11/02/2022 |
| GPU | N/A | N/A | 1x Intel® Arc A770™ 16GB, 512 EU |
1x Intel® Iris® Xe Graphics |
1x Intel® Data Center GPU Flex 170 |
1x Intel® Arc A770™ 16GB, 512 EU |
| Workload: Codec, resolution, frame rate Model, size (HxW), BS |
Yolov8m Model – input size [640, 640], batch 1 FP16 | int8 |
Yolov8m Model – input size [640, 640], batch 1 FP16 | int8 |
Yolov8m Model – input size [640, 640], batch 1 FP16 | int8 |
Yolov8m Model – input size [640, 640], batch 1 FP16 | int8 |
Yolov8m Model – input size [640, 640], batch 1 FP16 | int8 |
Yolov8m Model – input size [640, 640], batch 1 FP16 | int8 |
| TDP | 350W | 15W | 45W | 235W | 45W | 45W |
| OpenVINO Version | 2023.0.0-10926 | 2023.0.0-10926 | 2023.0.0-10926 | 2023.0.0-10926 | 2023.0.0-10926 | 2023.0.0-10926 |
| Benchmark Date | May 31, 2023 | May 29, 2023 | June 15, 2023 | May 29, 2023 | June 15, 2023 | May 29, 2023 |
| Benchmarked by | Intel Corporation | Intel Corporation | Intel Corporation | Intel Corporation | Intel Corporation | Intel Corporation |
- Learn more about OpenVINO
- Explore OpenVINO’s documentation