An egocentric pointing gesture dataset.

Codebase and Public EgoGestAR Dataset: https://github.com/varunj/EgoGestAR

Public Testing Dataset: https://github.com/varunj/EgoGestAR/tree/master/testvideo

• The left column shows standard input gesture sequences shown to the users before the data collection.
• The right column depicts the variation in the data samples.

These gestures could be applied to different use cases.

• The black block depicts swipe gestures (Left, Right, Up, Down) for list interaction.
• Green block showing Rectangle and Circle gestures for Region of Interest (RoI) highlighting.
• Red block (Checkmark: Yes, Caret: No, X: Delete, Star: Bookmark) gestures for evidence capture (in, say, industrial AR applications.)

The highlighted point in each gesture indicates the starting position of the gesture.

• Lists 240 gesture videos captured in varying environments, lighting conditions and by various users.
• 22 videos per class and additional 20 random hand movement videos.
• File naming convention: testvideo_gesturename_serialnumber.mp4

• Lists 500 gesture inputs used for testing. 50 gestures per class.
• File naming convention: test_gesturename_serialnumber.txt

• Lists 500 gesture images corresponding to the inputs. Plotted in black and white with a circular dot representing the starting of the gesture.
• File naming convention: test_gesturename_serialnumber.png

• Lists the time taken (in seconds) to perform each test gesture.

• Lists 2000 gesture inputs used for training. 200 gestures per class.
• File naming convention: train_gesturename_serialnumber.txt

• Lists 2000 gesture images corresponding to the inputs. Plotted in black and white with a circular dot representing the starting of the gesture.
• File naming convention: train_gesturename_serialnumber.png

• Lists the time taken (in seconds) to perform each training gesture.

1. Run to generate ground truth files for gestures.

1. Run to generate ground truth for custom gestures and corresponding time taken to draw each gesture.
2. Gestures saved in train_.txt separated by '----------'.

1. Reads from the specified folder.
2. Generates a graphical representation of the gestures in 10 graphs (to interpret the gesture).

1. Accepts the input gesture during runtime.
2. Outputs as above in <4generate_results>.

1. Reads '-----------' separated gestures and stores as different files.

1. Outputs a histogram of the length of raw gestures captured.

1. Up/Down samples raw gestures to 200 length.

1. Requires Kivy (https://kivy.org/docs/installation/installation.html)