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CNN-RNN for human activity recognition

License: MIT License

Jupyter Notebook 37.51% Dockerfile 6.10% Python 56.39%

cnn-rnn's Introduction

CNN-RNN

Tensorflow based implementation of convolution-reccurent network for classification of human interactions on video.
Uses SDHA 2010 High-level Human Interaction Recognition Challenge dataset.

Requirements

Python 3.5.2
Tensorflow > 1.0
Python OpenCV > 3.0

Evaluation

Example:

python main.py --lrate 0.001 --update

Parameter	Default value	Description
`epoch`	1	Number of epoch
`esize`	50	Size of examples
`estep`	20	Length of step for grouping frames into examples
`height`	240	Height of frames
`width`	320	Width of frames
`lrate`	1e-4	Learning rate
`logdir`	network/logs	Path to store logs and checkpoints
`conv`	standard	Type of CNN block (inception/vgg16)
`rnn`	GRU	Type of RNN block (LSTM/GRU)
`update`	False	Re-Generate TFRecords
`download`	False	Download dataset
`restore`	False	Restore from previous checkpoint
`test`	False	Test evaluation

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