Blog

How To Make Your Own People Counting Computer Vision Application in short time

image

How To Make Your Own People Counting Computer Vision Application in short time

Computer vision best satisfies artificial intelligence tasks that would otherwise be solved with human eyesight. People counting, also known as crowd counting, is a common application of computer vision

In this article, I will

  • build my own people counting system using visual programming on viso.ai
  • show you step-by-step how to do so, and
  • will additionally review the Viso Builder interface as I walk you through the tutorial.

People counting is used to count people passing by or to estimate the number of people in an area, or the population density of a crowd. The statistics of people counts provide useful information for event detection or strategy planning for a moderated area.

What Should the People Counting System Do?

The people counting system I will build in this tutorial should be based on object detection with a goal of detecting people using neural networks. The objective is to build a system that counts people crossing over a predefined “line”, simulating the entrance of a retail store, as an example


People counting Use Case with Object Detection. Showing the box with the Region of Interest (ROI) and the crossing line.

The pre-trained CNN models currently available on Viso Suite were trained to detect people using the COCO dataset. With regard to the person images on the COCO dataset, they are about frontal-view, side-view, but there are only a few top-down views. Therefore, these pre-trained CNN models (YOLO, SSD-mobile, etc.) will work best for the frontal-view.

Build the App: Connect the Right Nodes the Right Way

Logged into Viso Suite I want to create my own people counting system using pre-trained models and tools that are provided off-the-shelf. This will be done in the Viso Builder, a visual programming interface for building computer vision applications.

The people counting system will contain several connected nodes, each performing a specific task towards accomplishing the final application.

1. Video Input: To get started, we need to configure the video source, or where the frames will come from. These settings will tell my application whether to read the frames from an IP camera, USB camera or from a video file. Capturing the frames from the right source is the very first step before passing the frames to the next node.
2. Region of Interest: In this step, I want to tell my application where inside the image the algorithm should be applied to and where the counting should take place. This will speed up the processing time and configure the counting area for the system. The Regions of Interest can be configured as rectangle, polygon or sections.
3. Object Detection: From the pre-processed frames I want to detect the objects of interest, in our case people. The Object Detection node allows me to select from several pre-trained AI models for different hardware architectures, using available AI accelerators such as VPU (e.g. Intel Neural Compute Stick 2) or TPU (Google Coral) out of the box.
4. Object Count: In this step we need to tell the system what to do with the detected persons, in our case counting. The Object Count node lets me define the analysis and aggregation interval of the detected objects and lets me set the upload interval to send the results to the cloud where it can be picked up and displayed in a dashboard.
5. Output Preview: The Video View node creates an end-point for showing the processed video stream including the detection and counting results in real-time. While this will not be needed for my system in production, it is a good way for debugging and tweaking certain parameters while testing.

The Viso Builder makes it easy to add nodes to an application. I simply drag and drop the nodes mentioned above into the workspace grid, and they are ready to be configured without the need of any additional programming.

For the system to work correctly, the nodes need to be connected in the right way. The video source should send the input frames to the Region of Interest (ROI) node to be further processed. At the same time, the frames should be sent to the Output Preview node where the results will be displayed for debugging. Hovering over the connection dots shows the output of each node which makes it simple to choose the right connections.

Configure the People Counting Application

After the nodes are connected using the Viso Builder canvas, I want to configure each node to suit my needs. While the Region of Interest (ROI) piece of the application will need to be configured using a separate configuration interface, all other nodes are configured in the Viso Builder directly

• Video Input: My camera source will be a video file I previously uploaded to my Viso Suite workspace for testing purposes. The video is used to demo a real-world setting and is available for free via the internet. It is simulating a real camera input and can later easily be changed to an IP or USB camera. For frame width, height and FPS I want to keep the original video settings which are 1920 x 1080px at 30 frames per second. The video input node will automatically resize the frames if these parameters are changed, or skip/duplicate frames respectively in case of a difference in the input FPS and the configured FPS value on the video input node.
• Region of Interest: First, I need to select the right type of ROI for my project. While section and polygon ROIs are more suitable for projects where we need to detect something inside or outside a certain area, the rectangle ROI fits best for people counting. Second, I want to make sure that the frame has the correct orientation and the “entrance” or “walking directions” are top-down. This way, it will be easier for the algorithm to detect people at a higher accuracy. This can be achieved by setting the correct angle while checking the changes in real-time. Third, I want to draw my rectangle and adjust the cross-line which will trigger the counts once a person crosses the line (for both directions). These settings can be changed later on to test different angles and configurations.
• Object Detection: The Object Detection node lets me define the algorithms and hardware architectures to be used for my system. Additionally, it allows me to set the objects of interest. In my case, I would like to test with a rather light model on a Vision Processing Unit (VPU) as an accelerator (the Intel Neural Compute Stick 2 In my case, I will use Intel Movidius Myriad X for model inference. I will test with SSD MobileNet V2 and select “persons” as my target objects to be detected. As for object tracking, I will use dlib’s implementation of the correlation tracking algorithm which is available with a single-click from the Object Detection node interface.
• Object Count: Now I want to configure that my collected data is aggregated every 15 minutes and sent to the cloud once per hour. I select the analysis and upload interval accordingly and the rest, including the secure connection to the managed cloud backend, will be configured automatically as I deploy the application.
• Output Preview: The last step, and this is optional but helpful for debugging, is configuring a local endpoint to check the video output in real-time. I set a desired URL such as /video and will be able to check the output preview using the device’s IP address and the URL I set in the Output Preview interface. I additionally check “keep ratio” to keep the original frame size in my Output Preview.

And that’s it! I can save my application, and it will create the first version ready to be deployed to an edge device of my choice.

Check the People Counting Result Preview

The people counting system is now ready to run. The output of the program can be reviewed with the Output Preview module which was added to the workflow. The short extract of the video shows what to expect from the application we’ve just built.

What’s Next?

Once the application is created successfully, it can be deployed to edge devices at the click of a button and the data can be sent to the cloud dashboard