眼动追踪校准方法：从主动校准到无校准
眼动追踪校准方法：从主动校准到无校准
引言
眼动追踪是DMS的核心功能之一，而校准是影响眼动追踪精度的关键环节。传统的主动校准方法需要用户配合，在驾驶场景中实施困难。无校准或免校准技术成为行业追求的目标。本文将深入探讨各种校准方法的原理、精度要求及实现方案。
校准方法分类
1. 主动校准
主动校准需要用户注视特定目标点：
1 | |
流程：
- 显示屏呈现校准点（通常5-9点）
- 用户依次注视各点
- 系统记录每点的眼动特征
- 建立眼动-注视点映射
优点：精度高（<1°）
缺点：用户体验差，驾驶前需额外操作
2. 隐式校准
隐式校准利用自然注视行为：
1 | |
方法：
- 道路标记（车道线、路牌）
- 后视镜注视
- 仪表盘查看
优点：无感知
缺点：精度较低，需时较长
3. 无校准
无校准依赖模型泛化能力：
1 | |
方法：
- 大规模数据训练的通用模型
- 角膜反射成像
- 几何模型估计
优点：零操作
缺点：精度受个体差异影响
校准流程详解
主动校准流程
import numpy as np
import cv2
from collections import defaultdict
class ActiveCalibration:
"""主动校准系统"""
# 校准点布局
CALIBRATION_POINTS = [
(0.1, 0.1), # 左上
(0.9, 0.1), # 右上
(0.5, 0.5), # 中心
(0.1, 0.9), # 左下
(0.9, 0.9) # 右下
]
def __init__(self, screen_size=(1920, 1080)):
"""初始化
Args:
screen_size: 屏幕尺寸
"""
self.screen_size = screen_size
self.calibration_data = defaultdict(list)
self.current_point_idx = 0
self.samples_per_point = 30
def get_current_point(self):
"""获取当前校准点
Returns:
point: (x, y) 屏幕坐标
"""
if self.current_point_idx >= len(self.CALIBRATION_POINTS):
return None
norm_x, norm_y = self.CALIBRATION_POINTS[self.current_point_idx]
x = int(norm_x * self.screen_size[0])
y = int(norm_y * self.screen_size[1])
return (x, y)
def add_sample(self, eye_features, point):
"""添加校准样本
Args:
eye_features: 眼部特征
point: 注视点坐标
"""
if self.current_point_idx >= len(self.CALIBRATION_POINTS):
return
self.calibration_data[self.current_point_idx].append({
'eye_features': eye_features,
'point': point
})
# 检查是否完成当前点
if len(self.calibration_data[self.current_point_idx]) >= self.samples_per_point:
self.current_point_idx += 1
def is_complete(self):
"""校准是否完成
Returns:
complete: 是否完成
"""
return self.current_point_idx >= len(self.CALIBRATION_POINTS)
def compute_calibration(self):
"""计算校准参数
Returns:
calibration_params: 校准参数
"""
if not self.is_complete():
return None
all_eye_features = []
all_points = []
for point_idx, samples in self.calibration_data.items():
for sample in samples:
all_eye_features.append(sample['eye_features'])
all_points.append(sample['point'])
eye_features = np.array(all_eye_features)
points = np.array(all_points)
# 多项式回归映射
# 简化示例：线性映射
# 实际使用更高阶多项式
from sklearn.linear_model import LinearRegression
model_x = LinearRegression()
model_y = LinearRegression()
model_x.fit(eye_features, points[:, 0])
model_y.fit(eye_features, points[:, 1])
return {
'model_x': model_x,
'model_y': model_y,
'eye_features_mean': np.mean(eye_features, axis=0),
'eye_features_std': np.std(eye_features, axis=0)
}
class ImplicitCalibration:
"""隐式校准系统"""
def __init__(self,
convergence_threshold=50,
min_samples=100):
"""初始化
Args:
convergence_threshold: 收敛阈值
min_samples: 最小样本数
"""
self.convergence_threshold = convergence_threshold
self.min_samples = min_samples
self.samples = []
self.calibration_params = None
def detect_salient_region(self, frame, head_pose):
"""检测显著区域（可作为校准锚点）
Args:
frame: 输入帧
head_pose: 头部姿态
Returns:
region: 显著区域信息
"""
# 检测可能的注视目标
# 例如：后视镜、仪表盘、道路标记
regions = []
# 后视镜检测
# 简化：假设后视镜在特定位置
mirror_pos = self._detect_rearview_mirror(frame)
if mirror_pos:
regions.append({
'type': 'rearview_mirror',
'position': mirror_pos,
'confidence': 0.8
})
# 仪表盘区域
if self._is_looking_at_dashboard(head_pose):
regions.append({
'type': 'dashboard',
'position': (960, 800), # 假设位置
'confidence': 0.6
})
return regions
def _detect_rearview_mirror(self, frame):
"""检测后视镜"""
# 简化实现
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150)
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
aspect_ratio = w / h
if 2.0 < aspect_ratio < 4.0 and 0.03 < w * h / (frame.shape[0] * frame.shape[1]) < 0.05:
return (x + w // 2, y + h // 2)
return None
def _is_looking_at_dashboard(self, head_pose):
"""判断是否在看仪表盘"""
if head_pose is None:
return False
pitch = head_pose.get('pitch', 0)
return -40 < pitch < -20
def update(self, eye_features, gaze_point, detected_region):
"""更新校准
Args:
eye_features: 眼部特征
gaze_point: 当前估计的注视点
detected_region: 检测到的显著区域
"""
if detected_region is None:
return
# 如果检测到显著区域且置信度够高
for region in detected_region:
if region['confidence'] > 0.7:
self.samples.append({
'eye_features': eye_features,
'assumed_point': region['position']
})
# 检查是否可以计算校准参数
if len(self.samples) >= self.min_samples:
self._compute_calibration()
def _compute_calibration(self):
"""计算校准参数"""
if len(self.samples) < self.min_samples:
return
# 使用RANSAC鲁棒回归
from sklearn.linear_model import RANSACRegressor
X = np.array([s['eye_features'] for s in self.samples])
y = np.array([s['assumed_point'] for s in self.samples])
ransac_x = RANSACRegressor()
ransac_y = RANSACRegressor()
ransac_x.fit(X, y[:, 0])
ransac_y.fit(X, y[:, 1])
self.calibration_params = {
'model_x': ransac_x,
'model_y': ransac_y
}
class CalibrationFreeGazeEstimator:
"""无校准眼动估计器"""
def __init__(self, model_path=None):
"""初始化
Args:
model_path: 预训练模型路径
"""
self.model = None
self.personalization_params = {}
def estimate_gaze(self, eye_image, head_pose):
"""估计注视点
Args:
eye_image: 眼部图像
head_pose: 头部姿态
Returns:
gaze_vector: 注视向量
confidence: 置信度
"""
# 几何模型方法
gaze_vector = self._geometric_estimation(eye_image, head_pose)
# 或深度学习方法
if self.model is not None:
dl_gaze = self._deep_learning_estimation(eye_image)
# 融合
gaze_vector = 0.5 * gaze_vector + 0.5 * dl_gaze
return gaze_vector, 0.8
def _geometric_estimation(self, eye_image, head_pose):
"""几何模型估计
Args:
eye_image: 眼部图像
head_pose: 头部姿态
Returns:
gaze_vector: 注视向量
"""
# 简化实现
# 实际需要：瞳孔中心检测、角膜反射检测
# 基于头部姿态的粗估计
yaw = head_pose.get('yaw', 0)
pitch = head_pose.get('pitch', 0)
# 转换为注视向量
gaze_x = np.sin(np.radians(yaw))
gaze_y = -np.sin(np.radians(pitch))
gaze_z = np.cos(np.radians(yaw)) * np.cos(np.radians(pitch))
return np.array([gaze_x, gaze_y, gaze_z])
def _deep_learning_estimation(self, eye_image):
"""深度学习估计"""
# 模型推理
# return self.model.predict(eye_image)
return np.array([0, 0, 1]) # 简化
def personalize(self, user_samples):
"""个性化适应
Args:
user_samples: 用户样本数据
"""
# 计算个性化偏移
if len(user_samples) < 5:
return
offsets = []
for sample in user_samples:
predicted = sample['predicted_gaze']
actual = sample['actual_gaze']
offsets.append(actual - predicted)
self.personalization_params['offset'] = np.mean(offsets, axis=0)
# 精度评估
class CalibrationEvaluator:
"""校准精度评估器"""
@staticmethod
def calculate_angular_error(predicted, actual, distance=600):
"""计算角度误差
Args:
predicted: 预测注视点
actual: 实际注视点
distance: 屏幕距离(mm)
Returns:
error: 角度误差(度)
"""
# 计算像素误差
pixel_error = np.sqrt((predicted[0] - actual[0])**2 + (predicted[1] - actual[1])**2)
# 转换为角度
# 假设屏幕PPI
ppi = 96
mm_per_pixel = 25.4 / ppi
error_mm = pixel_error * mm_per_pixel
angular_error = np.degrees(np.arctan(error_mm / distance))
return angular_error
@staticmethod
def evaluate_calibration(calibrator, test_data):
"""评估校准精度
Args:
calibrator: 校准器
test_data: 测试数据
Returns:
metrics: 评估指标
"""
errors = []
for sample in test_data:
eye_features = sample['eye_features']
actual_point = sample['point']
predicted_point = calibrator.predict(eye_features)
error = CalibrationEvaluator.calculate_angular_error(predicted_point, actual_point)
errors.append(error)
return {
'mean_error': np.mean(errors),
'std_error': np.std(errors),
'max_error': np.max(errors),
'accuracy_1deg': sum(1 for e in errors if e < 1) / len(errors) * 100,
'accuracy_2deg': sum(1 for e in errors if e < 2) / len(errors) * 100
}
# 使用示例
if __name__ == "__main__":
# 主动校准示例
print("=== 主动校准 ===")
active_cal = ActiveCalibration()
while not active_cal.is_complete():
point = active_cal.get_current_point()
print(f"请注视屏幕点: {point}")
# 模拟收集样本
for _ in range(30):
eye_features = np.random.randn(6) # 模拟眼部特征
active_cal.add_sample(eye_features, point)
params = active_cal.compute_calibration()
print(f"校准完成，参数已计算")
# 隐式校准示例
print("\n=== 隐式校准 ===")
implicit_cal = ImplicitCalibration()
# 模拟自然驾驶数据收集
for i in range(150):
eye_features = np.random.randn(6)
gaze_point = np.random.rand(2) * 1920
frame = np.random.randint(0, 255, (1080, 1920, 3), dtype=np.uint8)
head_pose = {'yaw': 0, 'pitch': -30}
regions = implicit_cal.detect_salient_region(frame, head_pose)
implicit_cal.update(eye_features, gaze_point, regions)
print(f"隐式校准样本数: {len(implicit_cal.samples)}")
# 无校准示例
print("\n=== 无校准方法 ===")
cf_estimator = CalibrationFreeGazeEstimator()
gaze, conf = cf_estimator.estimate_gaze(
None,
{'yaw': 15, 'pitch': -10}
)
print(f"注视向量: {gaze}, 置信度: {conf}")
眼动追踪校准方法：从主动校准到无校准
https://dapalm.com/2026/06/01/2026-06-01-眼动追踪校准方法从主动校准到无校准/