human estimation 训练测试trick

部署运行你感兴趣的模型镜像

训练

1、随机翻转(random flip)、随机旋转(random rotaion,-45~+45)、随机尺寸变换(random scale)来数据增强。

2、对于固定大小的输入,先扩展到固定的长宽比,不改变长宽比进行裁剪,resize到固定尺寸。

3、Adam算法、BatchNormalization、学习率先大一点,再减少。

 

测试

1、多尺度、翻转图片 进行关键点估计,再平均化heatmaps的预测结果

2、对预测的heatmap进行gaussian filter

3、从最高响应到第二高相应的方向上的四分之一偏移量作为关键点的最终位置??

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Linly-Talker

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### 3D Human Pose Estimation Techniques and Applications In the realm of computer vision, **3D human pose estimation (HPE)** aims to identify and classify not only the presence but also the three-dimensional positions of key joints within the human body[^1]. This technology has evolved significantly with advancements in deep learning methods. #### Monocular Image-Based Methods Monocular image-based approaches leverage single-camera setups for estimating 3D poses from images or video frames. These models often employ convolutional neural networks (CNNs) that are trained on large datasets containing annotated keypoints. The network learns to predict depth information alongside spatial coordinates by understanding context clues such as limb orientation relative to camera angles[^2]. For instance, a popular method involves using hourglass architectures which iteratively refine heatmaps representing probable locations of each joint until convergence upon accurate predictions. Another approach utilizes multi-view geometry principles combined with CNN outputs to reconstruct full-body skeletons even when parts of bodies may be occluded during capture sessions. #### Multi-modal Fusion Approaches Beyond traditional visual data sources like RGB cameras, researchers have explored integrating other sensing modalities into HPE systems. One notable example includes leveraging WiFi signals capable of penetrating obstacles including walls; this allows for non-line-of-sight tracking without requiring line-of-sight visibility between subjects and sensors. By training deep neural networks on synchronized wireless and visual inputs, these hybrid solutions can achieve comparable accuracy levels while extending operational capabilities beyond conventional limitations imposed by purely optical means alone. #### Real-world Applications The practical implications span across various domains: - **Healthcare**: Monitoring patient movements post-surgery recovery. - **Sports Science**: Analyzing athlete performance metrics accurately. - **Virtual Reality/Augmented Reality**: Enhancing user interaction experiences through realistic avatar animations driven directly off real-time motion captures. ```python import numpy as np from sklearn.model_selection import train_test_split def preprocess_data(images, labels): """Preprocesses input dataset.""" X_train, X_val, y_train, y_val = train_test_split( images, labels, test_size=0.2, random_state=42) return X_train, X_val, y_train, y_val class PoseEstimator: def __init__(self): self.model = None def fit(self, X_train, y_train): # Train model here... pass def evaluate(self, X_val, y_val): # Evaluate model performance... pass ```
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