People-Counting-in-Real-Time

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

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

Refer to added Features. Also, added support for an IP camera.

• 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.

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Table of Contents

• Simple Theory
• Running Inference
• Features
• References
• Next Steps

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 2 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 example, mobiles, ip cameras, scanners etc.
• Thus, SSD seasoned with a MobileNet should theoretically result in a faster, more efficient object detector.

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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

• First up, install all the required Python dependencies:

pip install -r requirements.txt

The requirements will be updated timely, but note that there can always be version conflicts between the dependencies themselves and other factors like OS, hardware etc.

• To run inference on a test video file, head into the directory/use the command:

python run.py --prototxt mobilenet_ssd/MobileNetSSD_deploy.prototxt --model mobilenet_ssd/MobileNetSSD_deploy.caffemodel --input videos/example_01.mp4

• To run inference on an IP camera, first setup your camera url in 'mylib/config.py':

# Enter the ip camera url (e.g., url = 'http://191.138.0.100:8040/video')
url = ''

• Then run with the command:

python run.py --prototxt mobilenet_ssd/MobileNetSSD_deploy.prototxt --model mobilenet_ssd/MobileNetSSD_deploy.caffemodel

Set url = 0 for webcam.

Features

The following is an example of the added features. Note: You can easily on/off them in the config. options (mylib/config.py):

1. Real-Time alert:

• If selected, we send an email alert in real-time. Use case: If the total number of people (say 10 or 30) exceeded in a store/building, we simply alert the staff.
• You can set the max. people limit in config. (Threshold = 10).
• This is pretty useful considering the COVID-19 scenario.

• Note: To setup the sender email, please refer the instructions inside 'mylib/mailer.py'. Setup receiver email in the config.

2. Threading:

• Multi-Threading is implemented in 'mylib/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 for solid performance on complex real-time applications. To use threading:

set Thread = True in config.

3. Scheduler:

• Automatic scheduler to start the software. Configure to run at every second, minute, day, or Monday to Friday.
• This is extremely useful in a business scenario, for instance, you can run it only at your desired time (9-5?).
• Variables and memory would be reset == less load on your machine.

##Runs at every day (9:00 am). You can change it.
schedule.every().day.at("9:00").do(run)

4. Timer:

• Configure stopping the software after a certain time, e.g., 30 min or 9 hours from now.
• All you have to do is set your desired time and run the script.

if Timer:
	# Automatic timer to stop the live stream. Set to 8 hours (28800s).
	t1 = time.time()
	num_seconds=(t1-t0)
	if num_seconds > 28800:
		break

5. Simple log:

• Logs all data at end of the day.
• Useful for footfall analysis.

References

Main:

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

Optional:

• https://towardsdatascience.com/review-ssd-single-shot-detector-object-detection-851a94607d11
• https://pypi.org/project/schedule/

Next steps

• Train the SSD on human data (with a top-down view).
• Experiment with other detectors and benchmark the results on computationally less expensive embedded hardware.
• Evaluate the performance on multiple IP cameras.

 

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Thanks for the read & have fun!

To get started/contribute quickly (optional) ...

• Option 1

  • 🍴 Fork this repo and pull request!

• Option 2

  • 👯 Clone this repo:

  $ git clone https://github.com/saimj7/People-Counting-in-Real-Time.git
  

• Roll it!

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saimj7/ 19-08-2020 © Sai_Mj.