Social Distancing Detection Software

Social distancing in Real-Time using live video stream/IP camera in OpenCV library.

Output	Output

Applications

• Estimating the number of people in the stores, buildings, shopping malls, etc. in real-time.
• Sending an alert to the staff if the people exceed the social distancing limits.
• Optimizing the real-time stream for better performance (with threading).

Theory

Object detection:

• We will be using YOLOv3, trained on COCO dataset for object detection.
• In general, single-stage detectors like YOLO tend to be less accurate than two-stage detectors (R-CNN) but are significantly faster.
• YOLO treats object detection as a regression problem, taking a given input image and simultaneously learning bounding box coordinates and corresponding class label probabilities.
• It is used to return the person prediction probability, bounding box coordinates for the detection, and the centroid of the person.

Distance calculation:

• NMS (Non-maxima suppression) is also used to reduce overlapping bounding boxes to only a single bounding box, thus representing the true detection of the object. Having overlapping boxes is not exactly practical and ideal, especially if we need to count the number of objects in an image.
• Euclidean distance is then computed between all pairs of the returned centroids. Simply, a centroid is the center of a bounding box.
• Based on these pairwise distances, we check to see if any two people are less than/close to 'N' pixels apart.

Running Inference

• Install all the required Python dependencies:

pip install -r requirements.txt

• If you would like to use GPU, set USE_GPU = True in the config. options at 'mylib/config.py'.

• Note that you need to build OpenCV with CUDA (for an NVIDIA GPU) support first

• Download the weights file from here and place it in the 'yolo' folder.

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

python run.py -i mylib/videos/test.mp4

• 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

• Set url = 0 for webcam.

How does the Code work?

Kindly refer to the Documentation.pdf file that has been provided along with this repository.

References

Main:

• YOLOv3 paper: https://arxiv.org/pdf/1804.02767.pdf
• YOLO original paper: https://arxiv.org/abs/1506.02640
• YOLO TensorFlow implementation (darkflow): https://github.com/thtrieu/darkflow

Additional:

• More info: https://www.pyimagesearch.com/2018/11/12/yolo-object-detection-with-opencv/

Contributors

• Information gathered from this article
• Code Inspiration from Sai Subhakar T. Thank you for your information on this topic!
• Manish Shankar
• Adithya Sineesh