PHYSICAL VARIANCE DETECTION AUTOMATA

Number: 20260162401
Published: 2026-06-11
Filed: 2024-12-11
Assignee: DISNEY ENTERPRISES, INC.
Inventors: TATUM, III; James H., DE ARTE; Jason Erinn, COX; Jason Alexander
CPC: G06V10/751; G06V10/761; G06V10/774; G06V10/82; G06V20/70
Verdict: Low Notable software
First reported: 2026-W29 (2026-07-15)
Source: Google Patents · FreePatentsOnline

The keeper's note

The present invention sets forth a technique for performing automated physical variance detection.

Abstract

The present invention sets forth a technique for performing automated physical variance detection. The technique includes recording, via a capture device, a sample representation of a scene including one or more objects and selecting a baseline representation of the scene from a baseline database. The technique also includes generating, via a machine learning model, a variance probability value associated with each of one or more pixels included in the sample representation. The technique further includes generating a variance label associated with the sample representation and transmitting at least the sample representation and the variance label to the capture device.

Background

BACKGROUND Field of the Various Embodiments

Embodiments of the present disclosure relate generally to computer vision and, more specifically, to techniques for performing automatic physical variance detection in a scene including one or more objects. Description of the Related Art

Physical variance detection refers to the comparison of two or more representations of a physical scene and the detection of one or more differences between the representations of the scene. For example, a physical variance detection technique may determine that one or more objects included in a baseline representation of a scene may be missing from a subsequently acquired sample representation of the same scene. A physical variance detection technique may also determine that one or more objects included in a sample representation of a scene are not present in an earlier baseline representation of the scene. In addition to detecting missing or newly added objects, variance detection techniques may further determine that one or more objects present in both a baseline representation and a sample representation of a scene have experienced a change in position, orientation, and/or appearance between the baseline and sample representations. Physical variance detection techniques are useful for, e.g., comparing a current configuration of objects included in an amusement park attraction to a known, proper baseline configuration of the attraction. Physical variance detection techniques may als

Claims

1. A computer-implemented method for performing automated variance detection, the computer-implemented method comprising: recording, via a capture device, a sample representation of a scene including one or more objects; selecting a baseline representation of the scene from a baseline database; generating, via a machine learning model, a variance probability value associated with each of one or more pixels included in the sample representation; generating a variance label associated with the sample representation; and transmitting at least the sample representation and the variance label to the capture device. || 11. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: recording, via a capture device, a sample representation of a scene including one or more objects; selecting a baseline representation of the scene from a baseline database; generating, via a machine learning model, a variance probability value associated with each of one or more pixels included in the sample representation; generating a variance label associated with the sample representation; and transmitting at least the sample representation and the variance label to the capture device. || 19. A system comprising: one or more memories for storing instructions; and one or more processors for executing the instructions to: generate, via a machine learning model, one or more measures of variance associated with a training pair of representations of a scene, wherein the training pair of representations includes a baseline representation of the scene and a sample representation of the scene, and wherein the one or more measures of variance are based on differences between the baseline representation and the sample representation; generate one or more loss values based on the one or more measures of variance and one or more training annotations associated with the training pair of representations; and modify the machine learning model based on the one or more loss values.