Requirements

• Keras 2.0.3
• Tensorflow >= 1.0 (used 1.1.0)

Download dataset

• Download datafrom from: http://rpg.ifi.uzh.ch/davis_data.html
• Unzip and copy to /event_data/raw_data foler (see dataset_location_1.png, dataset_location2.png)

Create train/test data

• cd dataset_script
• python CREATE_DATA.py
• change list_scene inside CREATE_DATA.py for other scene

Run train script:

• cd main_keras
• python train.py --gpu GPU_ID --scene SCENCE_ID
• For example: python train.py --gpu 0 --scene shapes_rotation --> will train on GPU 0 and shapes_rotation sequence

Predict & evaluate

• cd main_keras
• python predict.py
• Change in_scene_id, in_net_id, in_model_file_name in this file for different scene

If you find this code useful in your research, please consider citing:

@article{nguyen6dof_lstmpose,
  author    = {Anh Nguyen and
			   Thanh{-}Toan Do and
			   Darwin G. Caldwell and
			   Nikos G. Tsagarakis},
  title     = {Real-Time Pose Estimation for Event Cameras with Stacked Spatial {LSTM}
			   Networks},
  journal   = {IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year      = {2019}
 }