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fall_detection module

The fall_detection module contains the FallDetectorLearner class, which inherits from the abstract class Learner.

Class FallDetectorLearner

Bases: engine.learners.Learner

The FallDetectorLearner class contains the implementation of a rule-based fall detector algorithm. It can be used to perform fall detection on images (inference) using a pose estimator or perform fall detection straight on poses.

This rule-based method can provide cheap and fast fall detection capabilities when pose estimation is already being used. Its inference time cost is ~0.1% of pose estimation, adding negligible overhead.

However, it has known limitations due to its nature. Working with 2D poses means that depending on the orientation of the person, it cannot detect most fallen poses that face the camera. Another example of known false-positive detection occurs when a person is sitting with their knees detectable, but ankles obscured or undetectable, this however is critical for detecting a fallen person whose ankles are not visible.

The FallDetectorLearner class has the following public methods:

FallDetectorLearner constructor

FallDetectorLearner(self, pose_estimator)

Constructor parameters:

  • pose_estimator: object, default=None
    Optionally provided pose estimator class used to detect poses that the fall detector uses to determine if a person has fallen.

FallDetectorLearner.initialize

FallDetectorLearner.initialize(self, pose_estimator)

This method is used to initialize the fall detector with a pose estimator, in case one was not provided in the constructor.

Parameters:

  • pose_estimator: object
    Provided pose estimator class used to detect poses that the fall detector uses to determine if a person has fallen.

FallDetectorLearner.eval

FallDetectorLearner.eval(self, dataset, verbose)

This method is used to evaluate the naive fall detector algorithm on an evaluation dataset. Returns a dictionary containing statistics regarding the evaluation.

Parameters:

  • dataset: object
    Object that holds the evaluation dataset. Can be of type ExternalDataset or a custom dataset inheriting from DatasetIterator.
  • verbose: bool, default=True
    If set to True, enables the maximum verbosity.

FallDetectorLearner.infer

FallDetectorLearner.infer(self, input_)

This method is used to perform fall detection on provided input. The input can be either an opendr.engine.data.Image object, an OpenCV image type (ndarray) or a list of poses. The list of poses input is helpful when the pose estimator is run externally. Returns a list of tuples, one for every person detected, that each contains an engine.target.Category, and the corresponding pose that was detected. It returns an empty list if no pose detections were made.

The engine.target.Category is 1 if person has fallen, -1 if person is standing and 0 if a person is detected, but the algorithm is unable to detect if person is standing or fallen.

Parameters:

  • input_: object
    Object of type engine.data.Image, ndarray or a list of poses.

FallDetectorLearner.download

FallDetectorLearner.download(path, mode, verbose, url)

Download utility for downloading fall detection test images and annotations.

Parameters:

  • path: str, default=None
    Local path to save the files, defaults to '.' if None.
  • mode: str, default="test_data"
    What files to download, can only be "test_data"
  • verbose: bool, default=False
    Whether to print messages in the console.
  • url: str, default=OpenDR FTP URL
    URL of the FTP server.

Examples

  • Inference using the pose estimator internally and result drawing example on a test image using OpenCV.

    import cv2
from opendr.engine.data import Image
from opendr.perception.fall_detection import FallDetectorLearner
from opendr.perception.pose_estimation import LightweightOpenPoseLearner
from opendr.perception.pose_estimation import draw, get_bbox

pose_estimator = LightweightOpenPoseLearner(device="cuda", mobilenet_use_stride=False)
pose_estimator.download(verbose=True)  # Download the default pretrained mobilenet model
pose_estimator.load("openpose_default")

fall_detector = FallDetectorLearner(pose_estimator)

# Download a sample dataset
fall_detector.download(verbose=True)

img = Image.open("test_images/fallen.png")
detections = fall_detector.infer(img)
fallen = detections[0][0].data  # Get fallen int from first detection
pose = detections[0][1]  # Get pose from first detection
img = img.opencv()
draw(img, pose)  # Draw the detected pose

if fallen == 1:
    x, y, w, h = get_bbox(pose)
    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
    cv2.putText(img, "Detected fallen person", (5, 12), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)
cv2.imshow('Result', img)
cv2.waitKey(0)

  • Inference using the pose estimator externally, passing a list of poses to infer and result drawing example on a test image using OpenCV.

    import cv2
from opendr.engine.data import Image
from opendr.perception.fall_detection import FallDetectorLearner
from opendr.perception.pose_estimation import LightweightOpenPoseLearner
from opendr.perception.pose_estimation import draw, get_bbox

pose_estimator = LightweightOpenPoseLearner(device="cuda", mobilenet_use_stride=False)
pose_estimator.download(verbose=True)  # Download the default pretrained mobilenet model
pose_estimator.load("openpose_default")

fall_detector = FallDetectorLearner()

# Download a sample dataset
fall_detector.download(verbose=True)

img = Image.open("test_images/fallen.png")
poses = pose_estimator.infer(img)
detections = fall_detector.infer(poses)
fallen = detections[0][0].data  # Get fallen int from first detection
pose = detections[0][1]  # Get pose from first detection
img = img.opencv()
draw(img, pose)  # Draw the detected pose

if fallen == 1:
    x, y, w, h = get_bbox(pose)
    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
    cv2.putText(img, "Detected fallen person", (5, 12), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)
cv2.imshow('Result', img)
cv2.waitKey(0)