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Joint-Based Action Progress Prediction.

Davide Pucci¹, Federico Becattini^1,2, Alberto Del Bimbo¹

¹Media Integration and Communication Center (MICC), University of Florence, 50124 Firenze, Italy.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This summary is machine-generated.

This study predicts action progress using body joints, a novel approach in computer vision. This method offers a lightweight and effective way to understand action evolution from raw pixels.

Keywords:

action progress prediction body joints body pose

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Area of Science:

Computer Vision
Machine Learning

Background:

Action understanding is crucial for surveillance and robotics.
Existing methods focus on action localization and recognition, not evolution.
Action progress prediction estimates how far an action is performed.

Purpose of the Study:

To propose a novel method for action progress prediction using body joints.
To leverage the precise pose information from body joints for effective action characterization.
To develop a lightweight and efficient approach for action progress estimation.

Main Methods:

Utilizing human body joints as the primary modality for action progress prediction.
Integrating keypoint and action information modules.
Enabling direct processing from raw pixels.

Main Results:

Demonstrated the effectiveness of body joints for action progress prediction.
Showcased a model that exploits body joints for characterizing action evolution.
Validated the proposed method on the Penn Action Dataset.

Conclusions:

Body joints provide a lightweight and effective modality for action progress prediction.
The proposed method offers a novel approach to understanding action evolution in computer vision.
This research advances the field of action understanding by focusing on temporal progression.