People-Counting-in-Real-Time

People Counting in Real-Time using live video stream/IP camera in OpenCV.

NOTE: This is an improvement/modification to https://www.pyimagesearch.com/2018/08/13/opencv-people-counter/

Live demo

The primary aim is to use the project as a business perspective, ready to scale.
Use case: counting the number of people in the stores/buildings/shopping malls etc., in real-time.
Sending an alert to the staff if the people are way over the limit.
Automating features and optimising the real-time stream for better performance (with threading).
Acts as a measure towards footfall analysis and in a way to tackle COVID-19 scenarios.

Simple Theory
- SSD detector
- Centroid tracker
Running Inference
Features
References

Simple Theory

SSD detector

We are using a SSD Single Shot Detector with a MobileNet architecture. In general, it only takes a single shot to detect whatever is in an image. That is, one for generating region proposals, one for detecting the object of each proposal.
Compared to other two shot detectors like R-CNN, SSD is quite fast.
MobileNet, as the name implies, is a DNN designed to run on resource constrained devices. For e.g., mobiles, ip cameras, scanners etc.
Thus, SSD seasoned with a MobileNet should theoretically result in a faster, more efficient object detector.

Centroid tracker

Centroid tracker is one of the most reliable trackers out there.
To be straightforward, the centroid tracker computes the centroid of the bounding boxes.
That is, the bounding boxes are (x, y) co-ordinates of the objects in an image.
Once the co-ordinates are obtained by our SSD, the tracker computes the centroid (center) of the box. In other words, the center of an object.
Then an unique ID is assigned to every particular object deteced, for tracking over the sequence of frames.

Running Inference

Install the dependencies

First up, install all the required Python dependencies by running: pip install -r requirements.txt

NOTE: Supported Python version is 3.11.3 (there can always be version conflicts between the dependencies, OS, hardware etc.).

Test video file

To run inference on a test video file, head into the root directory and run the command:

python people_counter.py --prototxt detector/MobileNetSSD_deploy.prototxt --model detector/MobileNetSSD_deploy.caffemodel --input utils/data/tests/test_1.mp4

Webcam

To run on a webcam, set "url": 0 in utils/config.json and run the command:

python people_counter.py --prototxt detector/MobileNetSSD_deploy.prototxt --model detector/MobileNetSSD_deploy.caffemodel

IP camera

To run on an IP camera, setup your camera url in utils/config.json, e.g., "url": 'http://191.138.0.100:8040/video'.

Then run the command:

python people_counter.py --prototxt detector/MobileNetSSD_deploy.prototxt --model detector/MobileNetSSD_deploy.caffemodel

Features

The following features can be easily enabled/disabled in utils/config.json:

{
    "Email_Send": "",
    "Email_Receive": "",
    "Email_Password": "",
    "url": "",
    "ALERT": false,
    "Threshold": 10,
    "Thread": false,
    "Log": false,
    "Scheduler": false,
    "Timer": false
}

Real-Time alert

If selected, we send an email alert in real-time. Example use case: If the total number of people (say 10 or 30) are exceeded in a store/building, we simply alert the staff.

You can set the max. people limit in config, e.g., "Threshold": 10.
This is quite useful considering scenarios similar to COVID-19. Below is an example:

1. Setup your emails:

In the config, setup your sender email "Email_Send": "" to send the alerts and your receiver email "Email_Receive": "" to receive the alerts.

2. Setup your password:

Similarly, setup the sender email password "Email_Password": "".

Note that the password varies if you have secured 2 step verification turned on, so refer the links below and create an application specific password:

Google mail has a guide here: https://myaccount.google.com/lesssecureapps
For 2 step verified accounts: https://support.google.com/accounts/answer/185833

Threading

Multi-Threading is implemented in utils/thread.py. If you ever see a lag/delay in your real-time stream, consider using it.
Threading removes OpenCV's internal buffer (which basically stores the new frames yet to be processed until your system processes the old frames) and thus reduces the lag/increases fps.
If your system is not capable of simultaneously processing and outputting the result, you might see a delay in the stream. This is where threading comes into action.
It is most suitable to get solid performance on complex real-time applications. To use threading: set "Thread": true, in config.

Scheduler

Automatic scheduler to start the software. Configure to run at every second, minute, day, or workdays e.g., Monday to Friday.
This is extremely useful in a business scenario, for instance, you could run the people counter only at your desired time (maybe 9-5?).
Variables and any cache/memory would be reset, thus, less load on your machine.

# runs at every day (09:00 am)
schedule.every().day.at("9:00").do(run)

Timer

Configure stopping the software execution after a certain time, e.g., 30 min or 8 hours (currently set) from now.
All you have to do is set your desired time and run the script.

# automatic timer to stop the live stream (set to 8 hours/28800s)
end_time = time.time()
num_seconds = (end_time - start_time)
if num_seconds > 28800:
    break

Simple log

Logs the counting data at end of the day.
Useful for footfall analysis. Below is an example:

References

Main:

SSD paper: https://arxiv.org/abs/1512.02325
MobileNets paper: https://arxiv.org/abs/1704.04861
Centroid tracker: https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/

Optional:

Object detection with SSD/MobileNets: https://pyimagesearch.com/2017/09/11/object-detection-with-deep-learning-and-opencv/
Schedule: https://pypi.org/project/schedule/

saimj7/People-Counting-in-Real-Time

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