监测人体姿势摔倒算法

lHHao

通过mediapipe来获取人体关节点，再分别通过重心下降检测(CGDD),身体倾斜检测(BTD)，外形轮廓变形检测(CSDD)进行当前状态分析，最后进行总体评估来分析是否摔倒!

[Python] 纯文本查看 复制代码

from collections import deque

class CGDD:# 
    def __init__(self, window_size=7, threshold=0.06):
        self.window_size = window_size
        self.threshold = threshold
        self.cog_history = deque(maxlen=window_size)

    def update(self, left_hip, right_hip):
        cog_y = (left_hip[1] + right_hip[1]) / 2
        self.cog_history.append(cog_y)

        if len(self.cog_history) < self.window_size:
            return False

        delta = self.cog_history[-1] - self.cog_history[0]
        return delta > self.threshold

[Python] 纯文本查看 复制代码

import math

class BTD:
    def __init__(self, angle_threshold=45):
        self.angle_threshold = angle_threshold

    def calculate_angle(self, shoulder_mid, hip_mid):
        dx = shoulder_mid[0] - hip_mid[0]
        dy = hip_mid[1] - shoulder_mid[1]
        angle = abs(math.degrees(math.atan2(dx, dy)))
        return angle

    def detect(self, shoulder_mid, hip_mid):
        angle = self.calculate_angle(shoulder_mid, hip_mid)
        return angle > self.angle_threshold, angle

[Python] 纯文本查看 复制代码

class SCDD:
    def __init__(self, ratio_threshold=0.9):
        self.ratio_threshold = ratio_threshold

    def detect(self, landmarks):
        xs = [lm[0] for lm in landmarks]
        ys = [lm[1] for lm in landmarks]

        width = max(xs) - min(xs)
        height = max(ys) - min(ys)

        if height == 0:
            return False, 0

        ratio = width / height
        return ratio > self.ratio_threshold, ratio

[Asm] 纯文本查看 复制代码

import cv2
from pose_detector import PoseDetector
from cgdd import CGDD
from btd import BTD
from scdd import SCDD
from posture_state import PostureState
import mediapipe as mp

pose_detector = PoseDetector()
cgdd = CGDD()
btd = BTD()
scdd = SCDD()
posture = PostureState()

# cap = cv2.VideoCapture(0)
cap = cv2.VideoCapture("这里传入是测试的视频，当然你也可以改成摄像头播放流地址")
while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    result = pose_detector.process(frame)

    if result.pose_landmarks:
        lm = result.pose_landmarks.landmark

        l_hip = (lm[mp.solutions.pose.PoseLandmark.LEFT_HIP].x,
                 lm[mp.solutions.pose.PoseLandmark.LEFT_HIP].y)
        r_hip = (lm[mp.solutions.pose.PoseLandmark.RIGHT_HIP].x,
                 lm[mp.solutions.pose.PoseLandmark.RIGHT_HIP].y)

        l_sh = (lm[mp.solutions.pose.PoseLandmark.LEFT_SHOULDER].x,
                lm[mp.solutions.pose.PoseLandmark.LEFT_SHOULDER].y)
        r_sh = (lm[mp.solutions.pose.PoseLandmark.RIGHT_SHOULDER].x,
                lm[mp.solutions.pose.PoseLandmark.RIGHT_SHOULDER].y)

        shoulder_mid = ((l_sh[0] + r_sh[0]) / 2,
                        (l_sh[1] + r_sh[1]) / 2)
        hip_mid = ((l_hip[0] + r_hip[0]) / 2,
                   (l_hip[1] + r_hip[1]) / 2)

        cgdd_flag = cgdd.update(l_hip, r_hip)
        btd_flag, angle = btd.detect(shoulder_mid, hip_mid)
        scdd_flag, ratio = scdd.detect([(lm[i].x, lm[i].y) for i in range(33)])
        state = posture.update((l_hip[1] + r_hip[1]) / 2, angle)
        # print(cgdd_flag)
        # print(btd_flag)
        # print(scdd_flag)
        # print(state)
        if cgdd_flag == True:
            cv2.putText(frame, "CGDD", (50, 50),
                        cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)
        if btd_flag == True:
             cv2.putText(frame, "BTD", (60, 60),
                        cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3)
        if scdd_flag == True:
             cv2.putText(frame, "SCDD", (100, 100),
                        cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 3)
        if state == "FALLEN" :
            cv2.putText(frame, "STATE FALLEN", (120, 120),
                        cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 3)



        pose_detector.draw(frame, result.pose_landmarks)

    cv2.imshow("Fall Detection", frame)
    if cv2.waitKey(1) & 0xFF == 27:
        break

cap.release()
cv2.destroyAllWindows()

已开源 https://github.com/hao-7835/Fall-detection-algorithm

rayyang

顶，我知道在什么地方可以用上

rayyang

linglong2013 发表于 2026-4-12 09:53
什么地方？

人形机器人

landon-zeng

感谢分享，正好项目里需要做摔倒报警，非常感谢

tshxxst

这个看起来就厉害

jiao250

这厉害呀！以后会有市场！

abslsp

这个厉害，以后会比较有用

IcePlume

这个很好，非常有用

leestrong

挺有意义的

lyrx

感谢分享，学到了

yangyyl2

有意思的东西。先收藏