This application is a smart IoT solution for counting people. It uses the Intel® OpenVINO™ software to run inference on a video. input and provide information on the people in a designated area by providing the number of people in the frame, average duration of people in frame and total count of people.

I carried out a little bit of research and after reading this article, I decided to go with faster rcnn v2 inception model. I had to use the OpenVINO™ Model Optimizer to convert the model to OpenVINO™ Intermediate Representation in order for it to be compatible with OpenVINO™. The steps are listed below :

1) Download the rcnn model from the tensorflow model zoo:

❍ wget http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz

2) Uncompress the file:

❍ tar -xvf faster_rcnn_inception_v2_coco_2018_01_28.tar.gz

3) Enter this directory:

❍ cd faster_rcnn_inception_v2_coco_2018_01_28

4) Run the OpenVINO™ Model Optimizer to get the Intermediate Representation of the model for OpenVINO™:

❍ python /opt/intel/openvino/deployment_tools/model_optimizer/mo.py --input_model faster_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb --tensorflow_object_detection_api_pipeline_config pipeline.config --reverse_input_channels --tensorflow_use_custom_operations_config /opt/intel/openvino/deployment_tools/model_optimizer/extensions/front/tf/faster_rcnn_support.json

I tested the rcnn model and this was my values after testing. The comparison was carried out by comparing latency and memory of the model before and after conversion to Intermediate representation.

Some of the use cases of this project is listed below:

1) It can be used to keep record of the number of people that visited an area.

2) It can help in limiting the number of people in a location

Lighting, model accuracy, and camera focal length/image size have different effects on a deployed edge model. The potential effects of each of these are as follows:

• If lighting is too dim or if model is to be used in the dark, more work would have to go into selecting the right model or including some custom layers to improve inference in low light conditions.

• If model accuracy is poor, then it would probably not identify everyonee passing through the frame and would end up being redundant and can lead to overcrowding.

• If focal length is increased, model might be too narrow and not capture the full frame, miss some people in the frame and if decreased can reduce inference power of model.

Custom layers are layers that are not included in the list of known layers. If your topology contains any layers that are not in the list of known layers, the Model Optimizer classifies them as custom. Intel® OpenVINO™ already contains extensions for custom layers used in TensorFlow Object Detection Model Zoo thus no additional processing was done to the custom layers.

I added an additional feature whereby an alarm starts blaring once the total count of people in the video passes a certain threshold. I also attempted adding a toggle for the camera but i couldn't get that working.

• 6th to 10th generation Intel® Core™ processor with Iris® Pro graphics or Intel® HD Graphics.
• OR use of Intel® Neural Compute Stick 2 (NCS2)
• OR Udacity classroom workspace for the related course

• Intel® Distribution of OpenVINO™ toolkit 2019 R3 release
• Node v6.17.1
• Npm v3.10.10
• CMake
• MQTT Mosca server

Utilize the classroom workspace, or refer to the relevant instructions for your operating system for this step.

• Linux/Ubuntu
• Mac
• Windows

Utilize the classroom workspace, or refer to the relevant instructions for your operating system for this step.

• Linux/Ubuntu
• Mac
• Windows

There are three components that need to be running in separate terminals for this application to work:

• MQTT Mosca server
• Node.js* Web server
• FFmpeg server

From the main directory:

• For MQTT/Mosca server:

  cd webservice/server
  npm install
  

• For Web server:

  cd ../ui
  npm install
  

  Note: If any configuration errors occur in mosca server or Web server while using npm install, use the below commands:

  sudo npm install npm -g 
  rm -rf node_modules
  npm cache clean
  npm config set registry "http://registry.npmjs.org"
  npm install
  

It is up to you to decide on what model to use for the application. You need to find a model not already converted to Intermediate Representation format (i.e. not one of the Intel® Pre-Trained Models), convert it, and utilize the converted model in your application.

Note that you may need to do additional processing of the output to handle incorrect detections, such as adjusting confidence threshold or accounting for 1-2 frames where the model fails to see a person already counted and would otherwise double count.

If you are otherwise unable to find a suitable model after attempting and successfully converting at least three other models, you can document in your write-up what the models were, how you converted them, and why they failed, and then utilize any of the Intel® Pre-Trained Models that may perform better.

From the main directory:

cd webservice/server/node-server
node ./server.js

You should see the following message, if successful:

Mosca server started.

Open new terminal and run below commands.

cd webservice/ui
npm run dev

You should see the following message in the terminal.

webpack: Compiled successfully

Open new terminal and run the below commands.

sudo ffserver -f ./ffmpeg/server.conf

Open a new terminal to run the code.

You must configure the environment to use the Intel® Distribution of OpenVINO™ toolkit one time per session by running the following command:

source /opt/intel/openvino/bin/setupvars.sh -pyver 3.5

You should also be able to run the application with Python 3.6, although newer versions of Python will not work with the app.

When running Intel® Distribution of OpenVINO™ toolkit Python applications on the CPU, the CPU extension library is required. This can be found at:

/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/

Depending on whether you are using Linux or Mac, the filename will be either libcpu_extension_sse4.so or libcpu_extension.dylib, respectively. (The Linux filename may be different if you are using a AVX architecture)

Though by default application runs on CPU, this can also be explicitly specified by -d CPU command-line argument:

python main.py -i resources/Pedestrian_Detect_2_1_1.mp4 -m your-model.xml -l /opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so -d CPU -pt 0.6 | ffmpeg -v warning -f rawvideo -pixel_format bgr24 -video_size 768x432 -framerate 24 -i - http://0.0.0.0:3004/fac.ffm

If you are in the classroom workspace, use the “Open App” button to view the output. If working locally, to see the output on a web based interface, open the link http://0.0.0.0:3004 in a browser.

To run on the Intel® Neural Compute Stick, use the -d MYRIAD command-line argument:

python3.5 main.py -d MYRIAD -i resources/Pedestrian_Detect_2_1_1.mp4 -m your-model.xml -pt 0.6 | ffmpeg -v warning -f rawvideo -pixel_format bgr24 -video_size 768x432 -framerate 24 -i - http://0.0.0.0:3004/fac.ffm

To see the output on a web based interface, open the link http://0.0.0.0:3004 in a browser.

Note: The Intel® Neural Compute Stick can only run FP16 models at this time. The model that is passed to the application, through the -m <path_to_model> command-line argument, must be of data type FP16.

To get the input video from the camera, use the -i CAM command-line argument. Specify the resolution of the camera using the -video_size command line argument.

For example:

python main.py -i CAM -m your-model.xml -l /opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so -d CPU -pt 0.6 | ffmpeg -v warning -f rawvideo -pixel_format bgr24 -video_size 768x432 -framerate 24 -i - http://0.0.0.0:3004/fac.ffm

To see the output on a web based interface, open the link http://0.0.0.0:3004 in a browser.

Note: User has to give -video_size command line argument according to the input as it is used to specify the resolution of the video or image file.

The servers herein are configured to utilize the Udacity classroom workspace. As such, to run on your local machine, you will need to change the below file:

webservice/ui/src/constants/constants.js

The CAMERA_FEED_SERVER and MQTT_SERVER both use the workspace configuration. You can change each of these as follows:

CAMERA_FEED_SERVER: "http://localhost:3004"
...
MQTT_SERVER: "ws://localhost:3002"