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gaze-estimation-adas-0002

Use Case and High-Level Description

This is a custom VGG-like convolutional neural network for gaze direction estimation.

Example and Gaze Vector Definition

The network takes three inputs: square crop of left eye image, square crop of right eye image, and three head pose angles - (yaw, pitch, and roll) (see figure). The network outputs 3-D vector corresponding to the direction of a person’s gaze in a Cartesian coordinate system in which z-axis is directed from person’s eyes (mid-point between left and right eyes’ centers) to the camera center, y-axis is vertical, and x-axis is orthogonal to both z,y axes so that (x,y,z) constitute a right-handed coordinate system.

Specification

Metric

Value

GFlops

0.139

MParams

1.882

Source framework

PyTorch*

Validation Dataset

Two random held out individuals from an internal dataset containing images of 60 people with different gaze directions.

Validation Results

The accuracy of gaze direction prediction is evaluated through the use of MAE of angle (in degrees) between the ground truth and predicted gaze direction.

Dataset

MAE, degrees

Standard deviation of AE, degrees

Internal dataset

6.95

3.58

Inputs

  1. Blob, name: left_eye_image, shape: 1, 3, 60, 60 in the format B, C, H, W, where:

    • B - batch size

    • C - number of channels

    • H - image height

    • W - image width

  2. Blob, name: right_eye_image, shape: 1, 3, 60, 60 in the format B, C, H, W, where:

    • B - batch size

    • C - number of channels

    • H - image height

    • W - image width

  3. Blob, name: head_pose_angles, shape: 1, 3 in the format B, C, where:

    • B - batch size

    • C - number of channels

Outputs

The net output is a blob with name gaze_vector and the shape: 1, 3, containing Cartesian coordinates of gaze direction vector. Please note that the output vector is not normalizes and has non-unit length.

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

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