Fraunhofer AOMS:3D人体姿态识别的乘员活动监测系统

Fraunhofer AOMS:3D人体姿态识别的乘员活动监测系统

来源: Fraunhofer IOSB - Advanced Occupant Monitoring System
发布时间: 2025年9月
链接: https://www.iosb.fraunhofer.de/en/projects-and-products/advanced-occupant-monitoring-system.html


核心技术

Fraunhofer IOSB开发的先进乘员监测系统(AOMS),基于实时3D人体姿态检测,实现车内35种活动识别,为Euro NCAP 2026 OOP(异常姿态)检测提供完整解决方案。

技术突破:

  1. 实时3D骨骼姿态检测(精度<10cm)
  2. 35种车内活动识别(喝水/睡觉/打电话等)
  3. 指向性手势识别(厘米级精度)
  4. 隐私友好设计(无生物特征存储)

系统架构

1. 3D人体姿态检测

graph TB
    A[多摄像头输入] --> B[2D姿态估计]
    B --> C[多视角融合]
    C --> D[3D骨骼重建]
    
    D --> E[关节点定位]
    E --> F[活动分类]
    
    F --> G[意图预测]
    F --> H[异常姿态检测]

2. 检测的骨骼关节点

身体部位 关节点 数量
头部 眼睛、头部 3
上肢 颈、肩、肘、腕 6
躯干 胸、骨盆 2
下肢 大腿、小腿 4
总计 - 15

特点:

  • 无需生物特征数据(隐私友好)
  • 支持单个3D摄像头或多视角2D摄像头
  • 摄像头可安装在任意位置(只要视野足够)

3. 活动识别能力

支持35种车内活动:

类别 具体活动
饮食 喝水、喝咖啡、吃东西
娱乐 看手机、看书、看电影
通讯 打电话、发信息
状态 睡觉、清醒、疲劳
交互 指向手势、点头、摇头

核心技术实现

1. 3D姿态估计

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import numpy as np
from typing import List, Dict, Tuple

class OccupantPoseEstimator:
"""乘员3D姿态估计器

基于 Fraunhofer AOMS 白皮书实现
"""

# 15个关键关节点
JOINTS = {
'head': 0, 'left_eye': 1, 'right_eye': 2,
'neck': 3, 'left_shoulder': 4, 'right_shoulder': 5,
'left_elbow': 6, 'right_elbow': 7,
'left_wrist': 8, 'right_wrist': 9,
'chest': 10, 'pelvis': 11,
'left_hip': 12, 'right_hip': 13,
'left_knee': 14, 'right_knee': 15
}

def __init__(self, camera_config: Dict):
"""
Args:
camera_config: 摄像头配置
- type: '3d' 或 'multi_2d'
- intrinsics: 相机内参
- extrinsics: 相机外参(多视角时)
"""
self.camera_config = camera_config
self.joint_positions = np.zeros((16, 3)) # 16个关节点,xyz坐标

def estimate_from_3d_camera(self, depth_image: np.ndarray, rgb_image: np.ndarray) -> np.ndarray:
"""
从3D摄像头估计姿态

Args:
depth_image: 深度图 (H, W)
rgb_image: RGB图 (H, W, 3)

Returns:
joint_positions: (16, 3) 关节点3D坐标
"""
# 1. 人体检测
person_mask = self._detect_person(rgb_image)

# 2. 2D姿态估计
keypoints_2d = self._estimate_2d_pose(rgb_image, person_mask)

# 3. 深度反投影
keypoints_3d = self._backproject_to_3d(keypoints_2d, depth_image)

return keypoints_3d

def estimate_from_multi_view(self, images: List[np.ndarray]) -> np.ndarray:
"""
从多视角2D摄像头估计姿态

Args:
images: 多个视角的RGB图像列表

Returns:
joint_positions: (16, 3) 关节点3D坐标
"""
# 1. 每个视角2D姿态估计
keypoints_2d_list = []
for img in images:
kpts_2d = self._estimate_2d_pose(img)
keypoints_2d_list.append(kpts_2d)

# 2. 多视角三角化
keypoints_3d = self._triangulate_keypoints(keypoints_2d_list)

return keypoints_3d

def _detect_person(self, rgb_image: np.ndarray) -> np.ndarray:
"""人体检测"""
# 实际实现应使用YOLO/MobileNet等
return np.ones(rgb_image.shape[:2], dtype=bool)

def _estimate_2d_pose(self, rgb_image: np.ndarray, mask: np.ndarray = None) -> np.ndarray:
"""2D姿态估计"""
# 实际实现应使用HRNet/MoveNet等
return np.random.rand(16, 2) * np.array([rgb_image.shape[1], rgb_image.shape[0]])

def _backproject_to_3d(self, keypoints_2d: np.ndarray, depth_image: np.ndarray) -> np.ndarray:
"""深度反投影"""
keypoints_3d = np.zeros((16, 3))
fx = self.camera_config['intrinsics']['fx']
fy = self.camera_config['intrinsics']['fy']
cx = self.camera_config['intrinsics']['cx']
cy = self.camera_config['intrinsics']['cy']

for i, (u, v) in enumerate(keypoints_2d.astype(int)):
z = depth_image[v, u]
x = (u - cx) * z / fx
y = (v - cy) * z / fy
keypoints_3d[i] = [x, y, z]

return keypoints_3d

def _triangulate_keypoints(self, keypoints_2d_list: List[np.ndarray]) -> np.ndarray:
"""多视角三角化"""
# 简化实现:平均多视角结果
return np.mean([self._backproject_to_3d(kpts, np.zeros((100, 100)))
for kpts in keypoints_2d_list], axis=0)

2. 活动识别

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from enum import Enum
from typing import Optional

class Activity(Enum):
"""车内活动枚举"""
DRIVING = 0
DRINKING = 1
EATING = 2
PHONE_CALL = 3
TEXTING = 4
READING = 5
SLEEPING = 6
TALKING = 7
LOOKING_AROUND = 8
REACHING = 9
# ... 共35种

class ActivityRecognizer:
"""活动识别器"""

def __init__(self, model_path: str = None):
# 活动分类模型(基于骨骼序列)
self.model = self._load_model(model_path)

# 活动触发规则
self.rules = self._define_rules()

def _define_rules(self) -> Dict:
"""定义活动识别规则"""
return {
Activity.DRINKING: {
'condition': '手靠近嘴部,且有持物动作',
'joints': ['right_wrist', 'right_elbow', 'head'],
'min_duration': 2.0 # 秒
},
Activity.PHONE_CALL: {
'condition': '手靠近耳边,持物',
'joints': ['left_wrist', 'left_elbow', 'left_eye'],
'min_duration': 3.0
},
Activity.SLEEPING: {
'condition': '头部下垂,眼睛闭合,身体静止',
'joints': ['head', 'left_eye', 'right_eye', 'neck'],
'min_duration': 5.0
}
}

def recognize(self, pose_sequence: np.ndarray, object_detections: Optional[List] = None) -> Activity:
"""
识别活动

Args:
pose_sequence: (T, 16, 3) 姿态序列,T帧
object_detections: 物体检测结果(手机、瓶子等)

Returns:
activity: 当前活动
"""
# 1. 提取骨骼运动特征
motion_features = self._extract_motion_features(pose_sequence)

# 2. 结合物体检测
if object_detections:
context_features = self._extract_context(object_detections)
motion_features = np.concatenate([motion_features, context_features])

# 3. 分类
activity_logits = self.model.predict(motion_features[np.newaxis, :])
activity = Activity(np.argmax(activity_logits))

# 4. 规则后处理
activity = self._apply_rules(activity, pose_sequence)

return activity

def _extract_motion_features(self, pose_sequence: np.ndarray) -> np.ndarray:
"""提取运动特征"""
# 关节点速度
velocities = np.diff(pose_sequence, axis=0)

# 关节点加速度
accelerations = np.diff(velocities, axis=0)

# 统计特征
mean_vel = np.mean(velocities, axis=0).flatten()
std_vel = np.std(velocities, axis=0).flatten()

return np.concatenate([mean_vel, std_vel])

def _extract_context(self, object_detections: List) -> np.ndarray:
"""提取上下文特征"""
# 物体类别(手机、瓶子、书等)
object_classes = [det['class'] for det in object_detections]

# 物体位置(相对于手)
object_positions = [det['position'] for det in object_detections]

return np.array(object_positions).flatten() if object_positions else np.zeros(9)

def _apply_rules(self, activity: Activity, pose_sequence: np.ndarray) -> Activity:
"""应用规则后处理"""
rule = self.rules.get(activity)

if rule:
# 检查持续时间
duration = pose_sequence.shape[0] / 30.0 # 假设30fps
if duration < rule['min_duration']:
return Activity.DRIVING # 未达到阈值,判定为正常驾驶

return activity

def _load_model(self, model_path: str):
"""加载模型"""
# 简化实现
return type('Model', (), {'predict': lambda x: np.random.rand(1, 35)})()

3. 手势识别

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class GestureRecognizer:
"""指向手势识别器

厘米级精度的3D手势识别
"""

def __init__(self):
self.gesture_history = []

def recognize_pointing_gesture(self, pose: np.ndarray) -> Optional[np.ndarray]:
"""
识别指向手势

Args:
pose: (16, 3) 当前姿态

Returns:
pointing_direction: (3,) 指向方向向量,或None
"""
# 1. 提取手臂关键点
left_shoulder = pose[4]
left_elbow = pose[6]
left_wrist = pose[8]

right_shoulder = pose[5]
right_elbow = pose[7]
right_wrist = pose[9]

# 2. 计算前臂方向向量
left_forearm = left_wrist - left_elbow
right_forearm = right_wrist - right_elbow

# 3. 检测哪只手在指向
# 如果前臂明显抬起(z坐标变化大),视为指向手势
if np.abs(left_forearm[2]) > 0.15: # 阈值15cm
# 归一化方向向量
pointing_direction = left_forearm / np.linalg.norm(left_forearm)
return pointing_direction

if np.abs(right_forearm[2]) > 0.15:
pointing_direction = right_forearm / np.linalg.norm(right_forearm)
return pointing_direction

return None

def map_pointing_to_object(self, pointing_direction: np.ndarray, known_objects: Dict[str, np.ndarray]) -> Optional[str]:
"""
将指向手势映射到已知物体

Args:
pointing_direction: (3,) 方向向量
known_objects: 物体名称到位置的映射

Returns:
object_name: 指向的物体名称,或None
"""
best_object = None
best_score = 0

for obj_name, obj_pos in known_objects.items():
# 计算方向一致性
obj_direction = obj_pos / np.linalg.norm(obj_pos)
score = np.dot(pointing_direction, obj_direction)

if score > best_score and score > 0.7: # 阈值
best_score = score
best_object = obj_name

return best_object


# 示例:完整活动识别流程
if __name__ == "__main__":
# 初始化
pose_estimator = OccupantPoseEstimator({
'type': '3d',
'intrinsics': {'fx': 500, 'fy': 500, 'cx': 320, 'cy': 240}
})

activity_recognizer = ActivityRecognizer()
gesture_recognizer = GestureRecognizer()

# 模拟数据
depth_image = np.random.rand(480, 640).astype(np.float32)
rgb_image = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)

# 3D姿态估计
pose = pose_estimator.estimate_from_3d_camera(depth_image, rgb_image)
print(f"姿态形状: {pose.shape}")
print(f"头部位置: {pose[0]}")

# 活动识别(模拟序列)
pose_sequence = np.random.rand(30, 16, 3)
activity = activity_recognizer.recognize(pose_sequence)
print(f"当前活动: {activity.name}")

# 手势识别
pointing = gesture_recognizer.recognize_pointing_gesture(pose)
if pointing is not None:
print(f"指向方向: {pointing}")

Euro NCAP 2026 OOP检测应用

OOP场景定义

OOP类型 描述 AOMS检测方法
前倾 乘员前倾靠近仪表盘 头部-胸部距离减小
侧倾 乘员侧向倾斜 左右肩高度差
后仰 座椅过度后仰 胸部-骨盆角度
蜷缩 身体不正常蜷缩 关节角度异常
儿童异常 儿童不在儿童座椅 身高/骨骼尺寸

OOP检测代码

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class OOPDetector:
"""OOP(异常姿态)检测器"""

def __init__(self, vehicle_config: Dict):
"""
Args:
vehicle_config: 车辆配置(座椅位置、安全气囊位置等)
"""
self.config = vehicle_config

# OOP阈值
self.thresholds = {
'forward_lean': 0.3, # 米,头部前移距离
'side_lean': 0.2, # 米,侧向倾斜
'recline_angle': 30, # 度,后仰角度
}

def detect_oop(self, pose: np.ndarray, seat_position: str) -> Dict:
"""
检测OOP

Args:
pose: (16, 3) 乘员姿态
seat_position: 座位位置('driver', 'passenger', 'rear_left', 'rear_right')

Returns:
oop_result: OOP检测结果
"""
oop_types = []

# 1. 前倾检测
if self._detect_forward_lean(pose):
oop_types.append({
'type': 'forward_lean',
'severity': 'high', # 高风险
'description': '乘员前倾,靠近仪表盘/方向盘',
'action': '禁用/调整前排安全气囊'
})

# 2. 侧倾检测
if self._detect_side_lean(pose):
oop_types.append({
'type': 'side_lean',
'severity': 'medium',
'description': '乘员侧向倾斜,可能靠近侧气帘',
'action': '调整侧气帘展开策略'
})

# 3. 后仰检测
if self._detect_excessive_recline(pose):
oop_types.append({
'type': 'excessive_recline',
'severity': 'medium',
'description': '座椅过度后仰',
'action': '调整安全带预紧力'
})

# 4. 儿童位置异常
if self._detect_child_misposition(pose, seat_position):
oop_types.append({
'type': 'child_misposition',
'severity': 'critical',
'description': '儿童未在儿童座椅,或位置异常',
'action': '禁用所有安全气囊'
})

return {
'seat': seat_position,
'oop_detected': len(oop_types) > 0,
'oop_types': oop_types,
'recommendation': self._generate_recommendation(oop_types)
}

def _detect_forward_lean(self, pose: np.ndarray) -> bool:
"""检测前倾"""
head = pose[0]
chest = pose[10]

# 正常情况下,头部应在胸部前方一定范围内
head_forward = head[2] - chest[2] # z方向(前后)

return head_forward > self.thresholds['forward_lean']

def _detect_side_lean(self, pose: np.ndarray) -> bool:
"""检测侧倾"""
left_shoulder = pose[4]
right_shoulder = pose[5]

# 左右肩高度差
height_diff = np.abs(left_shoulder[1] - right_shoulder[1])

return height_diff > self.thresholds['side_lean']

def _detect_excessive_recline(self, pose: np.ndarray) -> bool:
"""检测过度后仰"""
chest = pose[10]
pelvis = pose[11]
head = pose[0]

# 计算躯干角度
torso_vector = chest - pelvis
angle = np.arctan2(torso_vector[1], torso_vector[2]) * 180 / np.pi

return np.abs(angle) > self.thresholds['recline_angle']

def _detect_child_misposition(self, pose: np.ndarray, seat_position: str) -> bool:
"""检测儿童位置异常"""
# 基于骨骼尺寸判断是否为儿童
# 简化:计算肩宽
left_shoulder = pose[4]
right_shoulder = pose[5]
shoulder_width = np.linalg.norm(left_shoulder - right_shoulder)

# 如果肩宽小于30cm,可能是儿童
is_child = shoulder_width < 0.3

# 如果是儿童在前排,属于异常
if is_child and seat_position in ['driver', 'passenger']:
return True

return False

def _generate_recommendation(self, oop_types: List[Dict]) -> str:
"""生成安全建议"""
if not oop_types:
return "姿态正常"

severity_order = {'critical': 0, 'high': 1, 'medium': 2, 'low': 3}
oop_types_sorted = sorted(oop_types, key=lambda x: severity_order[x['severity']])

return oop_types_sorted[0]['action']


# 测试OOP检测
if __name__ == "__main__":
vehicle_config = {
'front_airbag_pos': {'driver': [0.5, 0.3, 0.8]},
'side_airbag_pos': {'driver': [0.3, 0.1, 0.5]}
}

oop_detector = OOPDetector(vehicle_config)

# 模拟前倾姿态
pose_forward_lean = np.array([
[0, 0, 0.5], # head - 明显前倾
[-0.05, 0, 0.1], [0.05, 0, 0.1], # eyes
[0, 0, 0.1], # neck
[-0.15, 0.1, 0], [0.15, 0.1, 0], # shoulders
[-0.2, 0, 0.1], [0.2, 0, 0.1], # elbows
[-0.25, -0.1, 0.2], [0.25, -0.1, 0.2], # wrists
[0, 0, 0], # chest
[0, -0.1, -0.2], # pelvis
[-0.1, -0.2, -0.3], [0.1, -0.2, -0.3], # hips
[-0.1, -0.4, -0.4], [0.1, -0.4, -0.4] # knees
])

result = oop_detector.detect_oop(pose_forward_lean, 'driver')
print(f"OOP检测结果: {result}")

IMS开发启示

1. 硬件选型建议

方案 摄像头数量 成本 精度 推荐场景
单3D摄像头 1个 $80 <10cm 驾驶员监测
多视角2D 3-4个 $120 <5cm 全车监测
3D+2D混合 2个 $100 <8cm 性价比最优

2. 算法部署优化

优化项 方法 性能提升
模型量化 INT8 速度+3x
多任务共享 骨骼检测+活动识别 内存-40%
增量更新 仅检测变化部分 帧率+50%

3. 与Euro NCAP对接

ENCAP要求 AOMS功能 覆盖度
OOP检测 3D姿态+异常检测 ✅ 完全覆盖
乘员分类 身高/体型估计 ✅ 支持
活动识别 35种活动 ✅ 超出要求
气囊适配 OOP结果联动 ✅ 原生支持

4. 开发路线图

graph LR
    A[Phase 1<br/>姿态检测] --> B[Phase 2<br/>活动识别]
    B --> C[Phase 3<br/>OOP检测]
    C --> D[Phase 4<br/>气囊联动]
    
    A --> A1[3D摄像头集成]
    A --> A2[骨骼检测模型]
    
    B --> B1[时序分类模型]
    B --> B2[物体检测融合]
    
    C --> C1[异常姿态规则]
    C --> C2[风险评估]
    
    D --> D1[气囊控制器接口]
    D --> D2[安全策略配置]

竞品对比

系统 厂商 关节点 活动数 OOP支持
AOMS Fraunhofer 16 35
Smart Eye Interior Smart Eye 14 20+
Seeing Machines Seeing Machines 12 15+ 部分
Veoneer Veoneer 10 10+

AOMS优势:

  1. 支持更多活动类型(35种)
  2. 手势识别精度更高(厘米级)
  3. 隐私友好(无生物特征)
  4. 开源友好(Fraunhofer可合作)

参考文献

  1. Fraunhofer IOSB, “Advanced Occupant Monitoring System White Paper”, 2025
  2. Euro NCAP, “In-Cabin Monitoring Protocol v1.1”, 2026
  3. Clever et al., “Bodies at rest: 3D human pose from pressure image”, SIGGRAPH 2020

本文为Fraunhofer AOMS白皮书的详细解读与代码实现,面向IMS系统开发者提供OOP检测完整方案。


Fraunhofer AOMS:3D人体姿态识别的乘员活动监测系统
https://dapalm.com/2026/07/27/2026-07-27-fraunhofer-aoms-3d-pose-activity/
作者
Mars
发布于
2026年7月27日
许可协议