Multi-view Action Recognition via 3D Pose Reconstruction
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Solution Overview
Problem
Existing action recognition systems face challenges in efficiently analyzing human actions captured by camera videos, particularly in complex environments with varying camera positions and angles, leading to unreliable and time-consuming manual monitoring.
Innovation Solution
A system that uses one or more processors to obtain multiple videos of subjects, track target subjects across multiple cameras, reconstruct a 3D model of the target subjects, and recognize their actions based on the reconstructed 3D poses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If multiple cameras are used to capture videos in complex environments, then the coverage and detection capability are improved, but the complexity of tracking and recognizing actions across multiple views increases
Solution Approach 1:
The patent transforms 2D video data from multiple cameras into 3D pose information by reconstructing spatial coordinates of body keypoints. This dimensional transformation enables the system to resolve ambiguities in action recognition by adding depth information, thereby improving detection accuracy while managing the complexity of multi-camera coordination through geometric reconstruction algorithms.
Solution Approach 2:
The patent introduces 3D pose reconstruction as an intermediary representation between raw multi-camera video data and final action recognition. This intermediate 3D skeletal model serves as a unified representation that simplifies the integration of data from multiple camera views and provides a standardized format for action classification, reducing the overall system complexity.
2Productivity
If manual monitoring is used to identify human actions in videos, then the system is simple to implement, but the process becomes time-consuming and unreliable
Solution Approach 1:
The patent implements an automated system that performs action recognition without human intervention. The system automatically processes video data from multiple cameras, reconstructs 3D poses, and classifies actions using machine learning models. This self-service approach eliminates the time-consuming and unreliable manual monitoring process while maintaining simplicity through automated pipelines.
Solution Approach 2:
The patent replaces the mechanical process of manual video review with an automated computational system. Instead of human observers watching and interpreting videos, the system uses computer vision algorithms to automatically extract features, reconstruct poses, and classify actions, thereby dramatically improving both speed and reliability of action identification.
3Measurement precision
If deep learning techniques are used for action recognition, then the accuracy is improved, but the requirement for additional training data increases
Solution Approach 1:
The patent performs 3D pose reconstruction as a preliminary step before action recognition. By pre-processing the video data into structured 3D skeletal representations, the system creates a standardized and enriched input format that contains explicit spatial and temporal information. This preliminary transformation reduces the amount of raw training data needed because the critical features are already extracted and organized, allowing deep learning models to learn more efficiently from compact representations.
Data Source
AI summary
Implementations generally perform robust multi-view multi-target action recognition using reconstructed 3-dimensional (3D) poses. In some implementations, a method includes obtaining a plurality of videos of a plurality of subjects in an environment, where at least one target subject of the plurality of subjects performs one or more actions in the environment. The method further includes tracking the at least one target subject across at least two cameras. The method further includes reconstructing a 3-dimensional (3D) model of the at least one target subject based on the plurality of videos and the tracking of the at least one target subject. The method further includes recognizing the one or more actions of the at least one target subject based on the reconstructing of the 3D model.