如何用动捕相机与RGB相机自制数据集训练测试convNet？

Hey Joshua, sounds like you’ve got a solid plan for building a custom pose estimation dataset and training a ConvNet—great work doing your homework on existing datasets and frameworks already! Let me break down some practical steps and tips to help you pull this off smoothly:

This is the foundation of your project—get this right, and training will be way less of a headache.

• Sync Motion Capture & RGB Data
  The biggest risk here is misalignment between your ground truth poses and RGB frames. If your hardware supports it, use hardware triggering (e.g., a sync signal sent to both the motion capture camera and RGB camera) to ensure perfect frame synchronization. If hardware sync isn’t an option, use high-precision timestamps from both devices to align frames post-capture. Don’t forget to do spatial calibration too: map the 3D motion capture coordinates to your RGB camera’s 2D pixel space using camera calibration tools (like OpenCV’s calibration functions).
• Standardize Annotation Format
  Convert your motion capture pose data into a format that’s easy for your ConvNet to parse. For example, if you’re doing human pose estimation, follow the structure used in datasets like LSP or HumanEva: store each sample’s keypoint coordinates (2D or 3D, depending on your task) in a JSON or CSV file, with a filename that matches the corresponding RGB image. This one-to-one mapping will make writing data loaders trivial.
• Split Your Dataset Properly
  Stick to a standard split (e.g., 70% training, 20% validation, 10% testing) and make sure samples from the same subject or scene don’t cross splits—this avoids data leakage and gives you a more accurate measure of your model’s real-world performance.

You’ve already explored TensorFlow, Caffe, and Matlab—here’s how to leverage them for your custom dataset:

• Pick Your Framework Wisely
  TensorFlow/Keras is my top pick here: its tf.data.Dataset API makes loading custom datasets a breeze, and there’s a massive community of pose estimation projects you can reference. Matlab is great for quick prototyping if you’re comfortable with its ecosystem, but deploying models built in Matlab is less flexible than TF. Caffe is solid but has a steeper learning curve these days.
• Build a Robust Data Loader
  Write a custom data generator that loads RGB images and their corresponding pose labels, then applies data augmentation (critical for small custom datasets). For pose tasks, augmentations need to be keypoint-aware: if you flip an image horizontally, you must also mirror the x-coordinates of your keypoints. Other useful augmentations include random cropping, brightness adjustments, and slight rotations—borrow ideas from how the Cats/Dogs dataset is augmented, but adapt them for pose.
• Start with a Proven Model
  Don’t build a ConvNet from scratch! Start with a model that’s already proven on pose datasets like LSP or HumanEva:
  • For 2D pose estimation: Try the Hourglass Network or SimpleBaseline—both are widely used and have open-source implementations you can tweak for your dataset.
  • For 3D pose estimation: Look into variants of PoseNet or models that fuse RGB and motion capture data directly.

• Use Standard Metrics
  To evaluate your model’s performance, use metrics that are standard in pose estimation. PCKh (Percentage of Correct Keypoints) is the go-to metric (used in LSP and HumanEva)—it measures how many predicted keypoints fall within a threshold distance of their ground truth counterparts. This will let you compare your model’s performance to state-of-the-art results on existing datasets.
• Visualize Results
  Nothing beats visualizing your model’s predictions to spot issues. Write a script that overlays predicted keypoints (and their connections) onto the original RGB images, then compare them side-by-side with the ground truth poses. This will help you identify if your model is struggling with specific body parts or scenarios, so you can adjust your dataset or model accordingly.

You’ve already researched a ton of datasets—here’s how to apply their lessons to your project:

• HumanEva’s syncing method is gold: they use hardware triggers to align motion capture and RGB frames perfectly. If you can replicate this, you’ll eliminate a huge source of error.
• LSP’s annotation format is simple and scalable: each sample has a straightforward JSON entry with keypoint coordinates. Copy this structure to keep your dataset organized.
• FLIC focuses on upper-body pose—if your project only needs to track specific body parts, don’t waste time annotating the entire body. Narrowing your scope will speed up dataset creation and improve model performance.

内容的提问来源于stack exchange，提问作者Joshua Willman