Social Visual Behavior Autism

Question 1

Three-brach-track network

Answer 1

1. Head detection: detects heads in video frames
2. Head Tracking: Uses a linking algorithm to link detected heads into tracks for proccessing
3. Three-brach-tracking:
   Head-pose-branches (2 branches): processes sequences of head crops to infer gaze direction
   Head-Map-brach: encodes relative head positions, distances and the presence of other individuals.

Question 1

Study Overview

Question 2

Mutual Gaze Score graph

Answer 2

The result of the mutual gaze detection in four frames.

Question 3

Mutual Gaze/ Human Coded Ratio Plot

Answer 3

The scatterplot displaying th regression lines and confidence intervals of the two differnt pla groups

Question 4

Mutual Gaze Ratio

Answer 4

The proportion of frames where mutual gaze is detected (0 no mutual gaze, 1 constant mutual gaze)

Question 5

Hand coded ratio

Answer 5

Frequency of mutual gaze as hand coded by experts, serving as the ground truth

Question 6

What is the problem/motivation?

Answer 6

To introduce an social visual analytics behavior approach for observing the mutual gaze performance of children with autism

Question 6

How was data collected?

Answer 6

• Collected video data from autism therapy sessions (ASD Dataset). The ASD dataset was used as input for the mutual gaze detection framework.

Question 7

Discuss the Mutual Gaze Detection Framework

Answer 7

The development of the mutual Gaze detection framework is based on a three branch track network, a deep learning technique used to retrieve mutual gaze features between all possible pairs of participants in the video