donfaq / cnn-rnn

CNN-RNN for human activity recognition

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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

About

CNN-RNN for human activity recognition

License:MIT License


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