I am looking at ActionNet
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# Action Recognition
Action recognition classifies the activity, behavior, or gesture occuring over a sequence of video frames. The DNNs typically use image classification backbones with an added temporal dimension. For example, the ResNet18-based pre-trained models use a window of 16 frames. You can also skip frames to lengthen the window of time over which the model classifies actions.
The [`actionNet`](../c/actionNet.h) object takes in one video frame at a time, buffers them as input to the model, and outputs the class with the highest confidence. [`actionNet`](../c/actionNet.h) can be used from [Python](https://rawgit.com/dusty-nv/jetson-inference/master/docs/html/python/jetson.inference.html#actionNet) and [C++](../c/actionNet.h).
As examples of using the `actionNet` class, there are sample programs for C++ and Python:
- [`actionnet.cpp`](../examples/actionnet/actionnet.cpp) (C++)
- [`actionnet.py`](../python/examples/actionnet.py) (Python)
## Running the Example
To run action recognition on a live camera stream or video, pass in a device or file path from the [Camera Streaming and Multimedia](aux-streaming.md) page.
I have a scene that if someone do certain action, I want the Jetson board sends out a signal.
May I know if there is any link to train the ActionNet?
Do I just need to train the ImageNet and feed the onnx to actionNet?
@AK51, these models for ActionNet came from https://github.com/kn1ghtf1re/Activity-Recognition-TensorRT and I haven’t trained them myself before. I believe that they in turn got the models from https://github.com/kenshohara/3D-ResNets-PyTorch or used those training scripts (I haven’t tried those though)
Those models use a ResNet backbone, but they have added modifications from an image classifier in order to incorporate the third temporal dimension, hence simply training an ImageNet/ResNet classifier won’t work.
Alternatively, TAO Toolkit supports training of your own action models and can be deployed with DeepStream:
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