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.
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.
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.
- Install all the required Python dependencies:
pip install -r requirements.txt
- 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 a test video file and save video, 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 --output videos_out/video_out.avi
To run inference on an IP camera:
- 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.
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.
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/
- 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.
Based on : https://github.com/ShivamPrajapati2001/People_Counter