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Adding Knowledge to Unsupervised Algorithms for the Recognition of Intent.

Stuart Synakowski¹, Qianli Feng¹, Aleix Martinez¹

¹Dept. Electrical and Computer Eng., The Ohio State University Columbus, OH 43210.

International Journal of Computer Vision

|July 2, 2021

Summary

This summary is machine-generated.

This study introduces an unsupervised algorithm to detect intentional actions from agent 3D kinematics. The method leverages principles of motion to infer intentionality without requiring training data.

Keywords:

Action Recognition Commonsense Intent Theory of Mind Unsupervised

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics
Cognitive Science

Background:

Computer vision excels at visual tasks but struggles with higher-level human cognitive abilities like inferring intent.
Distinguishing intentional from unintentional actions is a key aspect of human-level understanding and theory of mind.

Purpose of the Study:

To develop an unsupervised algorithm for inferring agent intentionality from 3D kinematics.
To integrate knowledge of self-propelled and Newtonian motion into an intentionality detection system.

Main Methods:

Derived an algorithm based on the relationship between self-propelled motion and Newtonian motion.
Utilized 3D kinematics data as input for the algorithm.
Created three diverse datasets (abstract animations to realistic videos) for algorithm testing.

Main Results:

The unsupervised algorithm successfully recognized intentional and unintentional actions across datasets.
Performance was quantitatively comparable to supervised baseline methods.
Qualitative analysis demonstrated sensible intentionality segmentation.

Conclusions:

Integrating basic physics knowledge (motion principles) enables effective unsupervised intentionality inference.
The developed algorithm offers a novel approach to understanding agent behavior in computer vision.
This work advances AI's capability in interpreting complex actions and intent.