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Sai Subhakar T
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README.md
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README.md
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> This repo is a simple improvement/modification to https://www.pyimagesearch.com/2018/08/13/opencv-people-counter/
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<div align="center">
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<img src=https://imgur.com/tZYiOkt.gif" width=600>
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<p>Live demo</p>
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</div>
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- The primary aim is to use the project as a business perspective, ready to scale.
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- Use case: counting the number of people in the stores/buildings/shopping malls etc., in real-time.
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---
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## Table of Contents
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* [Theory](#theory)
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* [Simple Theory](#simple-theory)
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* [Running Inference](#running-inference)
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* [Features](#features)
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* [References](#references)
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* [Next Steps](#next-steps)
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## Theory
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## Simple Theory
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**SSD detector:**
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- 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.
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- Compared to other 2 shot detectors like R-CNN, SSD is quite fast.
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- MobileNet, as the name implies, is a DNN designed to run on resource constrained devices. For example, mobiles, ip cameras, scanners etc.
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- Thus, SSD seasoned with a MobileNet should theoretically result in a faster, more efficient object detector.
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---
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**Centroid tracker:**
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- Centroid tracker is one of the most reliable trackers out there.
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- To be straightforward, the centroid tracker computes the centroid of the bounding boxes.
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- That is, the bounding boxes are (x, y) co-ordinates of the objects in an image.
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- 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.
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- Then an unique ID is assigned to every particular object deteced, for tracking over the sequence of frames.
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## Running Inference
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- Install all the required Python dependencies:
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```
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pip install -r requirements.txt
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```
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- To run inference on a test video file, head into the directory/use the command:
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```
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python run.py --prototxt mobilenet_ssd/MobileNetSSD_deploy.prototxt --model mobilenet_ssd/MobileNetSSD_deploy.caffemodel --input videos/example_01.mp4
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```
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> To run inference on an IP camera:
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- Setup your camera url in 'run.py':
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```javascript
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# the following is an ip camera url example
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# just enter your camera url and it should work
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url = 'http://191.138.0.100:8040/video'
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vs = VideoStream(url).start()
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```
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- Then run with the command:
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```
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python run.py --prototxt mobilenet_ssd/MobileNetSSD_deploy.prototxt --model mobilenet_ssd/MobileNetSSD_deploy.caffemodel
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```
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## Features
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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':
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<img src="https://imgur.com/9hw1NP0.png" width=500>
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***1. Real-Time alert:***
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- 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.
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- This is pretty useful considering the COVID-19 scenario.
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<img src="https://imgur.com/35Yf1SR.png" width=400>
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- Note: To setup the sender email, please refer the instructions inside 'mylib/mailer.py'
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***2. Scheduler & Timer:***
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- 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?).
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- Stop running after a certain time, e.g., 30 min or 9 hours from now.
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```javascript
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##Runs at every day (9:00 am). You can change it.
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schedule.every().day.at("9:00").do(run)
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```
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```javascript
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if Timer:
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# Automatic timer to stop the live stream. Set to 8 hours (28800s).
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t1 = time.time()
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num_seconds=(t1-t0)
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if num_seconds > 28800:
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break
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```
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***3. Simple log:***
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- Logs all data at end of the day.
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- Useful for footfall analysis.
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<img src="https://imgur.com/CV2nCjx.png" width=400>
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## References
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***Main:***
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- SSD paper: https://arxiv.org/abs/1512.02325
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- MobileNet paper: https://arxiv.org/abs/1704.04861
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- Centroid tracker: https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/
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***Optional:***
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- https://towardsdatascience.com/review-ssd-single-shot-detector-object-detection-851a94607d11
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- https://pypi.org/project/schedule/
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## Next steps
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- Train the SSD on human data (with a top-down view).
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- Experiment with other detectors and benchmark the results on computationally less expensive embedded hardware.
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- Evaluate the performance on multiple IP cameras.
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<p> </p>
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---
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## Thanks for the read & have fun!
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> To get started/contribute quickly (optional) ...
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- **Option 1**
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- 🍴 Fork this repo and pull request!
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- **Option 2**
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- 👯 Clone this repo:
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```
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$ git clone https://github.com/saimj7/People-Counting-in-Real-Time.git
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```
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- **Roll it!**
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---
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saimj7/ 18-08-2020 © <a href="http://saimj7.github.io" target="_blank">Sai_Mj</a>.
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