HumanCount

This repository contains the final project's code for the Computer Vision course at Sapienza University of Rome (A.Y. 2021-22).

By Davide Quaranta, Davide Modenese, Federico Cernera.

The project does real-time human counting using HOG and SVM, and it is intended to be used to monitor a known area. In addition to counting, the program:

• Estimates the distance between people.
• Outputs visual and terminal alarms when:
  • The number of people is higher than the maximum allowed.
  • The minimum distance between people is less than the minimum allowed.
• Shows an evolving heat-map, to describe the density of the people over certain areas, over time.

In order to give better distance estimations, we needed to improve the size of the bounding boxes. To reach this goal, the approaches described in the paper Human Detection Using HOG-SVM, Mixture of Gaussian and Background Contours Subtraction by A. H. Ahmed, K. Kpalma and A. O. Guedi has been used as a reference.

A. H. Ahmed, K. Kpalma and A. O. Guedi, "Human Detection Using HOG-SVM, Mixture of Gaussian and Background Contours Subtraction," 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2017, pp. 334-338, doi: 10.1109/SITIS.2017.62.

Prerequisites

The only requirements to run the project are:

• OpenCV 4.5.3+
• NumPy

Running

The entry-point is the main.py script, which can be configured via multiple command line arguments; specifically:

usage: main.py [-h] -i INPUT [-sh] [-nf] [-m] [-nac] [-nad] [-s]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Input JSON
  -sh, --show-hog-boxes
                        Show the bounding boxes produced by HOG-SVM
  -nf, --no-filter-optimized-boxes
                        Also show the optimized bounding boxes that are outside the HOG-SVM ones
  -m, --use-mog2        Use MOG2 to perform background subtraction
  -nac, --no-alarm-count
                        Disable the alarm when the number of people exceedes the limit
  -nad, --no-alarm-distance
                        Disable the alarm when there is a distance smaller that the minimum limit allowed
  -s, --show            Show some intermediate preprocessing steps in a window

By default, the script looks for the still background image provided in the input JSON; alternatively, if the --use-mog2 option is provided, the background will be extracted in real-time for each frame.

The background image and the video to use must be in the same directory as the JSON file.

JSON configuration

The main script needs a JSON file as input, containing the configuration of the video/feed to use, for example:

{
    "video": "5.mp4",
    "background": "5.png",
    "camera_conf": {
        "height": 2.0,
        "lower_angle": 55,
        "upper_angle": 100
    },
    "alarms": {
        "max_people": 4,
        "min_distance": 1
    }
}

• If not available, the background image can be estimated from an already made video, by using the background_estimator.py script.
• The camera_conf section contains the known values of the camera, that are used to estimate the distance between the camera and the people.
• The alarms section contains parameters/rules to decide when to raise an alarm state.

Examples

This section contains some example, explaining the outcome of certain command line arguments.

# show the filtered optimized boxes (default)
python3 main.py -i datasets/5.json

# also show the hog-generated bounding boxes (blue)
python3 main.py -i datasets/6.json --show-hog-boxes

# also show the hog-generated bounding boxes (blue), and show the optimized ones even if they are not in an hog box (no filter)
python3 main.py -i datasets/7.json --show-hog-boxes --no-filter-optimized-boxes

# also show the hog-generated bounding boxes (blue),
# and some preprocessing steps in separate windows (segmentation, countours extraction)
python3 main.py -i datasets/8.json --show-hog-boxes --show

Terminal output

In addition to the visual signals (colored circles in the right bottom, and colored distances), alarms are logged to stdout.