跌倒检测_使用姿势估计的跌倒检测

跌倒检测

Fall detection has become an important stepping stone in the research of action recognition — which is to train an AI to classify general actions such as walking and sitting down. What humans interpret as an obvious action of a person falling face flat is but a sequence of jumbled up pixels for an AI. To enable the AI to make sense of the input it receives, we need to teach it to detect certain patterns and shapes, and formulate its own rules.

女不限检测已经成为动作识别的研究提供了重要的垫脚石-这是AI的训练进行分类的一般行为，如步行和坐下。 人类将其解释为人脸平躺时的明显动作，不过是AI的一系列混乱像素。 为了使AI能够理解接收到的输入，我们需要教它检测特定的图案和形状，并制定自己的规则。

To build an AI to detect falls, I decided not to go through the torture of amassing a large dataset and training a model specifically for this purpose. Instead, I used pose estimation as the building block.

为了构建能够检测跌倒的AI，我决定不经历收集大型数据集和为此目的专门训练模型的折磨。 相反，我使用姿势估计作为构建基块。

姿势估计 (Pose Estimation)

Pose estimation is the localisation of human joints — commonly known as keypoints — in images and video frames. Typically, each person will be made up of a number of keypoints. Lines will be drawn between keypoint pairs, effectively mapping a rough shape of the person. There is a variety of pose estimation methods based on input and detection approach. For a more in-depth guide to pose estimation, do check out this article by Sudharshan Chandra Babu.

姿势估计是人体关节(通常称为关键点)在图像和视频帧中的定位。 通常，每个人都将由多个关键点组成。 将在关键点对之间绘制线条，从而有效地绘制人的大致形状。 基于输入和检测方法的姿势估计方法有很多种。 有关姿势估计的更深入指南，请查看Sudharshan Chandra Babu的本文。

To make this model easily accessible to everyone, I chose the input as RGB images and processed by OpenCV. This means it is compatible with typical webcams, video files, and even HTTP/RTSP streams.

为了使该模型易于所有人使用，我选择了输入作为RGB图像并由OpenCV处理。 这意味着它与典型的网络摄像头，视频文件甚至HTTP / RTSP流兼容。

预训练模型 (Pretrained Model)

The pose estimation model that I utilised was OpenPifPaf by VITA lab at EPFL. The detection approach is bottom-up, which means that the AI first analyses the entire image and figures out all the keypoints it sees. Then, it groups keypoints together to determine the people in the image. This differs from a top-down approach, where the AI uses a basic person detector to identify regions of interest, before zooming in to identify individual keypoints. To learn more about how OpenPifPaf was developed, do check out their CVPR 2019 paper, or read their source code.

我使用的姿势估计模型是EPFL的VITA实验室的OpenPifPaf 。 该检测方法是自下而上的，这意味着AI首先分析整个图像并找出它看到的所有关键点。 然后，它将关键点分组在一起以确定图像中的人物。 这与自顶向下方法不同，在自顶向下方法中，AI使用基本人员检测器来识别感兴趣的区域，然后再放大以识别各个关键点。 要了解有关OpenPifPaf如何开发的更多信息，请查看其CVPR 2019论文或阅读其源代码。

多流输入 (Multi-Stream Input)

Most open-source models can only process a single input at any one time. To make this more versatile and scalable in the future, I made use of the multiprocessing library in Python to process multiple streams concurrently using subprocesses. This allows us to fully leverage multiple processors on machines with this capability.

大多数开源模型只能在任何时间处理单个输入。 为了将来使它更具通用性和可扩展性，我使用了Python中的