Human Action RecognitionWeek 4

Taylor Rassmann

Optical Flow

Optical flow is still being attained from the UCF50 dataset.

On action 35 out of 50 Currently at 900 GB of data

Kinematic Features

Code for 11 kinematic features complete with visualizations

Next Step: Create a system similar to the optical

flow code to attain the kinematic features from the entire dataset instead of single videos

Kinematic Features

Golf Swing

Kinematic Features

Symmetric Flow Field: captures the dynamics that emphasize the symmetry of a human action around a diagonal axis.

Kinematic Features

Divergence: captures the amount of local expansion taking place in the fluid.

Kinematic Features

Vorticity: the measure of local spin around the axis perpendicular to the plane of the flow field.

Current Work

Finish UCF50 optical flow computation Hopefully done by the end of the day

Apply Saad Ali’s old kinematic features and MIL code to UCF50 dataset

Implement vorticity and divergence kinematic features on UCF50 dataset

Determine which actions have global movement for use of COCOA

See if saliency maps increase accuracy when there is occlusion