多任务视线估计：应对光照变化和眼镜遮挡的鲁棒方案

多任务视线估计：应对光照变化和眼镜遮挡的鲁棒方案

论文信息

  • 标题: Multi-task driver gaze estimation in real world driving scenes
  • 期刊: Engineering Applications of Artificial Intelligence, 2025
  • 发表时间: 2025年8月9日
  • DOI: 10.1016/j.engappai.2025.110894

核心问题

实际驾驶场景中的视线估计面临多重挑战：

挑战 影响 传统方法表现
光照变化 夜间、隧道、进出阴影区域 精度下降 30-50%
眼镜遮挡 反光、框架遮挡眼睛区域 检测失败率 > 20%
相邻区域混淆 相邻视线区域难以区分 准确率 < 80%
个人差异 不同驾驶员的视线习惯 泛化能力差

核心创新

1. 多任务学习框架

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import torch
import torch.nn as nn
import torch.nn.functional as F

class MultiTaskGazeEstimator(nn.Module):
    """多任务视线估计网络
    
    任务：
    1. 视线区域分类（Gaze Zone Classification）
    2. 视线方向回归（Gaze Direction Regression）
    
    优势：
    - 区域分类提供粗粒度信息（左/右/仪表盘/后视镜等）
    - 方向回归提供细粒度信息（俯仰角、偏航角）
    - 联合训练提升泛化能力
    """
    
    def __init__(self, num_zones=9):
        super().__init__()
        
        # 共享骨干网络（轻量化 MobileNetV3）
        self.backbone = nn.Sequential(
            nn.Conv2d(3, 32, 3, stride=2, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            
            nn.Conv2d(32, 64, 3, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            
            nn.Conv2d(64, 128, 3, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            
            nn.Conv2d(128, 256, 3, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
        )
        
        # 任务头1：视线区域分类
        self.zone_classifier = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
            nn.Linear(256, 128),
            nn.ReLU(inplace=True),
            nn.Dropout(0.5),
            nn.Linear(128, num_zones)
        )
        
        # 任务头2：视线方向回归
        self.direction_regressor = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
            nn.Linear(256, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128, 2)  # (yaw, pitch) degrees
        )
        
        # 任务头3：眼睛状态检测（辅助任务）
        self.eye_state_detector = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
            nn.Linear(256, 64),
            nn.ReLU(inplace=True),
            nn.Linear(64, 2)  # (left_eye, right_eye) visibility
        )
        
    def forward(self, x):
        """
        Args:
            x: 输入图像, shape=(B, 3, H, W)
            
        Returns:
            zone_logits: 区域分类 logits, shape=(B, num_zones)
            direction: 视线方向 (yaw, pitch), shape=(B, 2)
            eye_state: 眼睛可见性, shape=(B, 2)
        """
        # 共享特征提取
        feat = self.backbone(x)
        
        # 多任务输出
        zone_logits = self.zone_classifier(feat)
        direction = self.direction_regressor(feat)
        eye_state = self.eye_state_detector(feat)
        
        return zone_logits, direction, eye_state


class MultiTaskLoss(nn.Module):
    """多任务损失函数
    
    L_total = L_zone + λ1 * L_direction + λ2 * L_eye_state
    
    权重调整策略：
    - 区域分类权重：1.0（主任务）
    - 方向回归权重：0.5（辅助任务）
    - 眼睛状态权重：0.3（辅助任务）
    """
    
    def __init__(self, zone_weight=1.0, direction_weight=0.5, eye_weight=0.3):
        super().__init__()
        self.zone_weight = zone_weight
        self.direction_weight = direction_weight
        self.eye_weight = eye_weight
        
        self.zone_loss = nn.CrossEntropyLoss()
        self.direction_loss = nn.MSELoss()
        self.eye_loss = nn.BCEWithLogitsLoss()
        
    def forward(self, zone_pred, zone_target, 
                direction_pred, direction_target,
                eye_pred, eye_target):
        
        # 区域分类损失
        loss_zone = self.zone_loss(zone_pred, zone_target)
        
        # 方向回归损失（仅在有标注时计算）
        valid_mask = (direction_target[:, 0] != -1)  # 假设 -1 表示无效
        if valid_mask.sum() > 0:
            loss_direction = self.direction_loss(
                direction_pred[valid_mask], 
                direction_target[valid_mask]
            )
        else:
            loss_direction = 0.0
        
        # 眼睛状态损失
        loss_eye = self.eye_loss(eye_pred, eye_target)
        
        # 总损失
        total_loss = (
            self.zone_weight * loss_zone +
            self.direction_weight * loss_direction +
            self.eye_weight * loss_eye
        )
        
        return total_loss, {
            'zone': loss_zone.item(),
            'direction': loss_direction.item() if isinstance(loss_direction, float) else loss_direction.item(),
            'eye': loss_eye.item()
        }


# 实际测试
if __name__ == "__main__":
    # 创建模型
    model = MultiTaskGazeEstimator(num_zones=9)
    
    # 模拟输入
    batch_size = 4
    x = torch.randn(batch_size, 3, 224, 224)
    
    # 推理
    zone_logits, direction, eye_state = model(x)
    
    print(f"区域分类输出: {zone_logits.shape}")
    print(f"视线方向输出: {direction.shape}")
    print(f"眼睛状态输出: {eye_state.shape}")
    
    # 测试损失
    criterion = MultiTaskLoss()
    zone_target = torch.randint(0, 9, (batch_size,))
    direction_target = torch.randn(batch_size, 2)
    eye_target = torch.rand(batch_size, 2)
    
    loss, loss_dict = criterion(zone_logits, zone_target, 
                                 direction, direction_target,
                                 eye_state, eye_target)
    
    print(f"\n总损失: {loss.item():.4f}")
    print(f"区域损失: {loss_dict['zone']:.4f}")
    print(f"方向损失: {loss_dict['direction']:.4f}")
    print(f"眼睛损失: {loss_dict['eye']:.4f}")

2. 光照鲁棒性增强

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class IlluminationAugmentation:
    """光照增强策略
    
    模拟实际驾驶场景的光照变化：
    1. 昼夜切换
    2. 隧道进出
    3. 阴影区域
    4. 逆光场景
    """
    
    def __init__(self):
        self.augmentations = [
            self.random_brightness,
            self.random_contrast,
            self.random_gamma,
            self.random_shadow,
            self.random_tunnel
        ]
        
    def random_brightness(self, img, p=0.5):
        """随机亮度调整"""
        if np.random.rand() < p:
            factor = np.random.uniform(0.3, 1.5)
            img = img * factor
            img = np.clip(img, 0, 255)
        return img
    
    def random_contrast(self, img, p=0.5):
        """随机对比度调整"""
        if np.random.rand() < p:
            factor = np.random.uniform(0.5, 1.5)
            mean = img.mean()
            img = (img - mean) * factor + mean
            img = np.clip(img, 0, 255)
        return img
    
    def random_gamma(self, img, p=0.3):
        """随机 Gamma 校正（模拟夜间场景）"""
        if np.random.rand() < p:
            gamma = np.random.uniform(0.5, 2.0)
            img = np.power(img / 255.0, gamma) * 255
            img = np.clip(img, 0, 255)
        return img
    
    def random_shadow(self, img, p=0.3):
        """随机阴影区域"""
        if np.random.rand() < p:
            h, w = img.shape[:2]
            # 创建阴影区域
            x1, y1 = np.random.randint(0, w//2), np.random.randint(0, h//2)
            x2, y2 = np.random.randint(w//2, w), np.random.randint(h//2, h)
            
            shadow = np.ones_like(img) * 0.7
            shadow[y1:y2, x1:x2] = 1.0
            
            img = img * shadow
            img = np.clip(img, 0, 255)
        return img
    
    def random_tunnel(self, img, p=0.2):
        """模拟隧道场景（整体变暗）"""
        if np.random.rand() < p:
            # 突然变暗（进出隧道）
            factor = np.random.uniform(0.3, 0.6)
            img = img * factor
            img = np.clip(img, 0, 255)
        return img
    
    def augment(self, img):
        """应用所有增强"""
        for aug in self.augmentations:
            img = aug(img)
        return img.astype(np.uint8)

3. 眼镜遮挡处理

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class GlassesOcclusionHandler:
    """眼镜遮挡处理
    
    策略：
    1. 眼镜检测（反射、框架）
    2. 眼镜区域分割
    3. 基于眼睛关键点的补偿
    """
    
    def __init__(self):
        # 眼镜反射特征
        self.specular_threshold = 200
        
    def detect_glasses(self, face_img, landmarks):
        """检测眼镜
        
        Args:
            face_img: 面部图像
            landmarks: 面部关键点
            
        Returns:
            has_glasses: 是否戴眼镜
            glasses_type: 眼镜类型（普通/墨镜）
        """
        # 提取眼睛区域
        left_eye = landmarks[36:42]
        right_eye = landmarks[42:48]
        
        # 检测反射
        left_eye_region = self._extract_region(face_img, left_eye)
        right_eye_region = self._extract_region(face_img, right_eye)
        
        # 反射检测
        left_specular = np.sum(left_eye_region > self.specular_threshold)
        right_specular = np.sum(right_eye_region > self.specular_threshold)
        
        # 判断
        specular_ratio = (left_specular + right_specular) / (
            left_eye_region.size + right_eye_region.size
        )
        
        has_glasses = specular_ratio > 0.1
        
        # 判断墨镜
        avg_brightness = (left_eye_region.mean() + right_eye_region.mean()) / 2
        glasses_type = 'sunglasses' if avg_brightness < 50 else 'normal'
        
        return has_glasses, glasses_type
    
    def _extract_region(self, img, points):
        """提取多边形区域"""
        from PIL import Image, ImageDraw
        
        # 创建 mask
        mask = Image.new('L', (img.shape[1], img.shape[0]), 0)
        draw = ImageDraw.Draw(mask)
        
        # 绘制多边形
        pts = [(int(p[0]), int(p[1])) for p in points]
        draw.polygon(pts, fill=255)
        
        # 应用 mask
        mask = np.array(mask)
        region = img * (mask[:, :, np.newaxis] if len(img.shape) == 3 else mask)
        
        return region
    
    def estimate_gaze_with_glasses(self, face_img, landmarks, base_gaze):
        """戴眼镜时的视线估计
        
        策略：
        1. 使用眼睛外轮廓代替虹膜
        2. 使用头部姿态补偿
        3. 增加时间平滑
        """
        has_glasses, glasses_type = self.detect_glasses(face_img, landmarks)
        
        if not has_glasses:
            return base_gaze
        
        # 补偿策略
        if glasses_type == 'sunglasses':
            # 墨镜：主要依赖头部姿态
            head_pose = self._estimate_head_pose(landmarks)
            compensated_gaze = self._pose_to_gaze(head_pose)
        else:
            # 普通眼镜：结合眼睛外轮廓
            eye_outer_corners = self._extract_eye_outer_corners(landmarks)
            compensated_gaze = self._estimate_from_outer_corners(eye_outer_corners)
        
        return compensated_gaze
    
    def _estimate_head_pose(self, landmarks):
        """估计头部姿态（简化实现）"""
        # 使用鼻尖、嘴角等关键点
        return {'yaw': 0.0, 'pitch': 0.0, 'roll': 0.0}
    
    def _pose_to_gaze(self, head_pose):
        """头部姿态转视线方向"""
        # 简化：假设视线与头部方向一致
        return {'yaw': head_pose['yaw'], 'pitch': head_pose['pitch']}
    
    def _extract_eye_outer_corners(self, landmarks):
        """提取眼睛外角"""
        left_outer = landmarks[36]
        right_outer = landmarks[45]
        return {'left': left_outer, 'right': right_outer}
    
    def _estimate_from_outer_corners(self, corners):
        """基于外角估计视线（简化）"""
        return {'yaw': 0.0, 'pitch': 0.0}

实验结果

1. 光照鲁棒性测试

场景 传统方法准确率 本文方法准确率 提升
白天正常光照 92.3% 93.1% +0.8%
夜间低光照 68.5% 89.7% +21.2%
隧道进出口 71.2% 91.3% +20.1%
阴影区域 74.6% 90.8% +16.2%
逆光场景 62.3% 88.5% +26.2%

2. 眼镜遮挡处理

眼镜类型 检测成功率 视线估计精度 RMSE (度)
无眼镜 - 93.1% 5.2
普通眼镜 96.5% 90.3% 6.8
墨镜 89.2% 82.7% 9.5
反光眼镜 91.8% 84.5% 8.7

3. 视线区域分类

区域 准确率 混淆主要区域
前方道路 96.2% 后视镜 (2.1%)
左后视镜 91.5% 左侧窗 (5.3%)
右后视镜 90.8% 右侧窗 (6.1%)
仪表盘 94.3% 中控屏 (3.8%)
中控屏 88.7% 仪表盘 (7.2%)
左侧窗 92.1% 左后视镜 (4.5%)
右侧窗 91.7% 右后视镜 (5.1%)
车顶 87.3% 后视镜 (8.9%)
腿部区域 85.6% 仪表盘 (10.2%)

IMS 开发启示

1. 视线估计模块架构

graph TB
    subgraph 输入处理
        A1[红外摄像头<br/>OV2311]
        A2[光照检测<br/>Histogram Analysis]
    end
    
    subgraph 预处理
        B1[光照归一化<br/>Retinex Algorithm]
        B2[眼镜检测<br/>Specular Analysis]
    end
    
    subgraph 多任务网络
        C1[骨干网络<br/>MobileNetV3]
        C2[区域分类<br/>9 zones]
        C3[方向回归<br/>yaw/pitch]
        C4[眼睛状态<br/>visibility]
    end
    
    subgraph 后处理
        D1[时间平滑<br/>Kalman Filter]
        D2[融合决策<br/>Zone + Direction]
    end
    
    A1 --> B1
    A2 --> B1
    B1 --> C1
    B2 --> C1
    C1 --> C2
    C1 --> C3
    C1 --> C4
    C2 --> D2
    C3 --> D2
    D2 --> D1

2. 部署优化

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class OptimizedGazeEstimator:
    """优化版视线估计器（边缘部署）"""
    
    def __init__(self, model_path, device='qcs8255'):
        # 加载 INT8 量化模型
        self.model = self._load_quantized_model(model_path, device)
        
        # 时间平滑滤波器
        self.kalman = self._init_kalman_filter()
        
        # 历史记录
        self.gaze_history = []
        self.history_size = 5
        
    def process_frame(self, frame):
        """处理单帧（完整流程）
        
        Args:
            frame: 红外图像, shape=(H, W, C)
            
        Returns:
            result: dict
                - zone: int (0-8)
                - direction: (yaw, pitch) in degrees
                - confidence: float
        """
        # 1. 光照检测
        illumination = self._detect_illumination(frame)
        
        # 2. 光照归一化
        normalized_frame = self._normalize_illumination(frame, illumination)
        
        # 3. 模型推理
        zone_logits, direction, eye_state = self.model(normalized_frame)
        
        # 4. 后处理
        zone = zone_logits.argmax().item()
        yaw, pitch = direction[0].item(), direction[1].item()
        
        # 5. 时间平滑
        smoothed_yaw, smoothed_pitch = self._apply_temporal_smoothing(yaw, pitch)
        
        # 6. 置信度计算
        confidence = self._calculate_confidence(zone_logits, eye_state)
        
        return {
            'zone': zone,
            'zone_name': self.ZONE_NAMES[zone],
            'direction': (smoothed_yaw, smoothed_pitch),
            'confidence': confidence
        }
    
    ZONE_NAMES = [
        'road_front', 'mirror_left', 'mirror_right',
        'dashboard', 'center_screen', 'window_left',
        'window_right', 'roof', 'lap'
    ]
    
    def _detect_illumination(self, frame):
        """检测光照水平"""
        # 简化：基于直方图
        gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
        hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
        
        # 平均亮度
        mean_brightness = gray.mean()
        
        # 分类
        if mean_brightness < 50:
            return 'night'
        elif mean_brightness < 100:
            return 'low'
        elif mean_brightness > 200:
            return 'bright'
        else:
            return 'normal'
    
    def _normalize_illumination(self, frame, illumination):
        """光照归一化（简化版 Retinex）"""
        if illumination == 'night':
            # 增强
            frame = cv2.convertScaleAbs(frame, alpha=2.0, beta=50)
        elif illumination == 'bright':
            # 压缩
            frame = cv2.convertScaleAbs(frame, alpha=0.7, beta=0)
        
        return frame
    
    def _apply_temporal_smoothing(self, yaw, pitch):
        """时间平滑（简单移动平均）"""
        self.gaze_history.append((yaw, pitch))
        
        if len(self.gaze_history) > self.history_size:
            self.gaze_history.pop(0)
        
        # 平均
        smoothed_yaw = np.mean([g[0] for g in self.gaze_history])
        smoothed_pitch = np.mean([g[1] for g in self.gaze_history])
        
        return smoothed_yaw, smoothed_pitch
    
    def _calculate_confidence(self, zone_logits, eye_state):
        """计算置信度"""
        zone_prob = torch.softmax(zone_logits, dim=1).max().item()
        eye_visibility = torch.sigmoid(eye_state).mean().item()
        
        confidence = zone_prob * 0.7 + eye_visibility * 0.3
        
        return confidence
    
    def _load_quantized_model(self, model_path, device):
        """加载量化模型（简化）"""
        # 实际需要 QNN/SNPE 加载
        return MultiTaskGazeEstimator()
    
    def _init_kalman_filter(self):
        """初始化卡尔曼滤波器"""
        # 简化实现
        return None

参考文献

  1. Zhang et al. (2025). Multi-task driver gaze estimation in real world driving scenes. Engineering Applications of Artificial Intelligence.

  2. Hu et al. (2025). FIFA: Fine-grained Inter-frame Attention for Driver’s Video Gaze Estimation. CVPR 2025.

总结

多任务视线估计方法通过联合学习区域分类和方向回归，在光照变化、眼镜遮挡等挑战场景下取得了显著提升。

IMS 开发启示：

  1. 多任务架构：区域分类 + 方向回归 + 眼睛状态，联合训练
  2. 光照鲁棒性：光照增强训练 + Retinex 归一化预处理
  3. 眼镜处理：反射检测 + 头部姿态补偿 + 外角估计
  4. 部署优化：INT8 量化 + 时间平滑 + 边缘推理

量化指标：

  • 夜间准确率：89.7%（传统方法 68.5%）
  • 眼镜场景准确率：90.3%（普通眼镜）
  • 边缘推理延迟：< 30ms（QCS8255）

论文来源：Engineering Applications of Artificial Intelligence 2025 | IMS 研究笔记

座舱监控
#Euro NCAP #DMS #IMS
多任务视线估计：应对光照变化和眼镜遮挡的鲁棒方案
https://dapalm.com/2026/08/16/2026-08-09-Multi-Task-Gaze-Estimation-Robust-Illumination/
作者
Mars
发布于
2026年8月16日
许可协议