Recurrent Pose Attention (RPAN)
Our Tensorflow implementation of Recurrent Pose Attention in Du et al.: "RPAN: An End-to-End Recurrent Pose-attention Network for Action Recognition in Videos".
Note that we are not associated with the original authors.
Tested with python 2.7. The following additional packages are required:
tensorflow, numpy, csv, cv2
Our simple RPAN model in
model_simple.py drops the parameter sharing method in Equation (2) of the paper. This is the version used in our submission for CVPR 2018 Moments in Time challenge.
We also attempt provide a model with the original parameter sharing scheme described. It can be found in
Pose Joint Maps
We provide an example on how to generate the joint maps in
load_pose_map(). Note that we use Openpose format (published as Cao et al.: "Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields") throughout the project. If you are using a different pose detector, you will need to modify the code.
video1.npy is an example of what
load_pose_map() expects. The format is Tx18x3, where each triplet is (x,y,confidence), and all values are in range [0,1]. In case there are multiple poses, we currently picked the one with highest confidence; if no pose is detected, we set the frame to all-zeros.
We assume that the video is stored as a collection of jpeg files, sampled at 25 fps. The files are organized as:
Unlike the published paper, we use ResNet v2-50 to extract the convolutional cube. You can download our ResNet weights at http://cmlab.csie.ntu.edu.tw/~agethen/resnet_v2.npy . Please do not forget to edit util.py and adjust the path to the ResNet weights.
For any feedback or questions, feel free to send a message to
s [dot] agethen [at] gmail [dot] com.