README.md

metadata

license: mit
tags:
  - video-understanding

Dataset of the paper Interacted Object Grounding in Spatio-Temporal Human-Object Interactions

Code: https://github.com/DirtyHarryLYL/HAKE-AVA

Preparing Dataset for HAKE-GIO + HAKE-AVA-PaSta (h box + b box & class + action + PaSta)

1. Dataset downloading steps

   1. Download AVA Dataset (following SlowFast).

      ./script/download_AVA_dataset.sh
      

   2. Downloading annotation

      The annotation is contained in GIO_annotation

      Please download it to ava folder and extract data from the package.

   3. Structure of downloaded data

      GIO
      |_ GIO_annotation
      |  |_ GIO_test.csv
      |  |_ GIO_train.csv
      |_ frames
      |  |_ [video name 0]
      |  |  |_ [video name 0]_000001.jpg
      |  |  |_ [video name 0]_000002.jpg
      |  |  |_ ...
      |  |_ [video name 1]
      |     |_ [video name 1]_000001.jpg
      |     |_ [video name 1]_000002.jpg
      |     |_ ...
      |_ frame_lists
      |  |_ train.csv
      |  |_ val.csv
      

2. Annotation Format

   Files in the GIO folder contains the annotations of each frame, including human/object box, action, object name, etc.

   example:

   video	frame	h_x1	h_y1	h_x2	h_y2	o_x1	o_y1	o_x2	o_y2	action	object_name	human_id	object_id
   -5KQ66BBWC4	905	0.392	0.033	0.556	0.618	0.37	0.019	0.432	0.608	6	stick	12	0
   -5KQ66BBWC4	906	0.408	0.008	0.586	0.639	0.37	0.036	0.457	0.678	6	stick	12	0
   -5KQ66BBWC4	907	0.42	0.115	0.616	0.883	0.371	0.143	0.466	0.878	6	stick	12	0

   The meanings of each column:

   • video: name of the video
   • frame: time (second) of the frame
   • h_x1~h_y2: the upper left and bottom right corners of human-box
   • o_x1~o_y2: the upper left and bottom right corners of object-box
   • action: the action label of the person in the human-box
   • object_name: name of object
   • human_id: ID of the person performing the action
   • object_id: category id of the object