# People-Counting-in-Real-Time
> People Counting in Real-Time using live video stream/IP camera in OpenCV.
> This repo is a simple improvement/modification to https://www.pyimagesearch.com/2018/08/13/opencv-people-counter/
![](https://imgur.com/tZYiOkt.gif")
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.
- Acts as a measure towards footfall analysis and in a way to tackle COVID-19.
---
## Table of Contents
* [Simple Theory](#simple-theory)
* [Running Inference](#running-inference)
* [Features](#features)
* [References](#references)
* [Next Steps](#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.
---
**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 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 an IP camera:
- Setup your camera url in 'run.py':
```javascript
# the following is an ip camera url example
# just enter your camera url and it should work
url = 'http://191.138.0.100:8040/video'
vs = VideoStream(url).start()
```
- Then run with the command:
```
python run.py --prototxt mobilenet_ssd/MobileNetSSD_deploy.prototxt --model mobilenet_ssd/MobileNetSSD_deploy.caffemodel
```
## Features
The following are some of the added features. Note: You can easily on/off them in the config options at the start of 'run.py':
***1. Real-Time alert:***
- If selected, we send an email alert in real-time. Use case: If the total number of people (say 30) exceeded in a store/building, we simply alert the staff.
- This is pretty useful considering the COVID-19 scenario.
- Note: To setup the sender email, please refer the instructions inside 'mylib/mailer.py'
***2. Scheduler & Timer:***
- Automatic scheduler to start the software. This is extremely useful in a business scenario, for instance, run it only at your desired time (9-5?).
- Stop running after a certain time, e.g., 30 min or 9 hours from now.
```javascript
##Runs at every day (9:00 am). You can change it.
schedule.every().day.at("9:00").do(run)
```
```javascript
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
```
***3. 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.
---
## 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!**
---
saimj7/ 19-08-2020 © Sai_Mj.