Comments (1)
Hi, I think there could be three reasons for bad performance: (1) The good action prediction relies on good pose estimation but pose estimation usually contains a lot of noise; (2) The actions defined by others (e.g., JHMDB) may not suitable for your desired action; (3) In wild body-related (not hands) action recognition case, RGB information is quite useful and it significantly improves the action prediction performance, it may be better to just partially depend on body skeleton.
If you only have one person in the video, you may find some ideas at https://www.pyimagesearch.com/2019/07/15/video-classification-with-keras-and-deep-learning/. If you have multiple people, I personally think it might be better to follow AVA detection related works at https://research.google.com/ava/. If you are working on hand gestures, I think DD-Net should be helpful, even the camera may produce the 5 FPS video files, you can use frames in the past few seconds and with a little prediction delay.
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Related Issues (20)
- Getting keypoints from video HOT 1
- accuracy changed in paper? HOT 4
- Tflite model HOT 1
- Unable to create conda env HOT 1
- Can I test the trained model of gesture recognition in RGB video? HOT 2
- confusion matrix doesn't match HOT 4
- training new gestures with unfixed length of input frames HOT 3
- Is this extensible to other 2D/3D skeleton annotations? HOT 2
- method to average results across J-HMDB splits HOT 1
- 88% accuracy on JHMDB HOT 1
- custom skeleton dataset and puppet scale HOT 3
- How to calculate puppet scale in custom dataset HOT 1
- Application of DD-Net on OpenPose/AlphaPose/... results HOT 2
- Considering classwise accuracy
- JHMDB dataset size
- About norm_scale function for JHMDB
- Scaling and selection of features?
- how to convert mediapipe to SHRED dataset format?
- SHREC dataset link
- SHREC 2017 Dataset
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