Human-Object Interaction Tracking with Monocular-IMU Pose Fusion
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Solution Overview
Problem
Human-object interaction (HOI) tracking systems face challenges in precision due to insufficient sensory information, particularly when only one camera is available, leading to issues like occlusion and scaling differences, which affect the accuracy of modeling human and object interactions.
Innovation Solution
A unified HOI tracking system utilizing an autoregressive architecture with post sampling to generate probabilistic pose distributions, integrating image data from a camera with motion data from sensors like IMUs, to improve tracking accuracy by leveraging real-time information and reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only one camera is used for HOI tracking, then device complexity is reduced, but measurement precision deteriorates due to insufficient sensory information
Solution Approach 1:
The patent combines data from multiple sources (monocular camera, IMU sensors, pose estimation models) into a unified tracking system. By merging image data with motion sensor data and probabilistic pose distributions, the system achieves high-precision tracking without requiring multiple cameras, thus reducing device complexity while maintaining measurement precision.
Solution Approach 2:
The patent introduces intermediate processing steps including pose distribution generation, sampling, and reconciliation between image data and motion data. These intermediary processes act as mediators that transform limited monocular input into accurate 3D pose estimates, compensating for the single-camera limitation and improving measurement precision without adding physical sensors.
2Device complexity
If monocular video data is used, then device complexity is reduced, but loss of information increases due to occlusion and scaling differences
Solution Approach 1:
The patent performs preliminary pose estimation and generates pose distributions before final tracking reconciliation. By pre-processing image data to create probabilistic pose hypotheses and preparing motion data in advance, the system compensates for information loss from occlusion and scaling issues, ensuring complete interaction tracking without adding sensors.
Solution Approach 2:
The patent implements a feedback mechanism where pose distributions from image data are reconciled with motion sensor data, and the combined information feeds back into refined pose estimation. This iterative feedback process recovers information lost to occlusion and scaling by continuously cross-validating visual and motion data, maintaining information completeness with minimal device complexity.
3Device complexity
If traditional HOI tracking methods are used, then device complexity is reduced, but manufacturing precision deteriorates due to error accumulation
Solution Approach 1:
The patent employs dynamic pose distribution sampling and iterative reconciliation processes that adapt to changing scene conditions. Rather than using static tracking methods, the system dynamically adjusts pose estimates by sampling from probability distributions and reconciling with motion data, preventing error accumulation and improving pose reconstruction accuracy without complicating the overall system architecture.
Solution Approach 2:
The patent transforms deterministic pose estimation into probabilistic pose distribution representation. By changing the parameter representation from fixed values to probability distributions and using sampling techniques, the system captures uncertainty and prevents error propagation, achieving high manufacturing precision in pose reconstruction while maintaining simple system architecture through software-based solutions.
Data Source
AI summary
A system and method for improving the accuracy of human-object interaction tracking includes a unified tracking system. The tracking system uses an autoregressive architecture to process incoming image data and motion data in real-time and generates mesh states and a pose distribution. Post sampling leverages motion data to select optimal samples from the pose distribution.