3D Hand Tracking Using Depth Sequences
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Solution Overview
Problem
Existing hand tracking systems are prone to drifting errors due to assumptions about hand depth and lack robustness against background variations, leading to high false positive rates and inability to accurately track hands in complex scenarios.
Innovation Solution
A background-invariant hand detector and bi-directional hand tracking algorithm are introduced, using depth sequences to segment pixels and track hands invariant to depth changes, with single-directional connectivity verification to reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hand tracking methods using depth maps are used, then hand detection can be performed, but the system drifts closer to the front of the scene and produces false positives when hands move behind the arm
Solution Approach 1:
The patent inverts the traditional unidirectional tracking approach by implementing bidirectional tracking that can detect hands both in front of and behind the arm. Instead of assuming hands are always in front, the system evaluates depth relationships in both directions, allowing accurate tracking regardless of hand position relative to the arm.
Solution Approach 2:
The system changes the depth parameter interpretation by introducing a bidirectional depth evaluation mechanism. Rather than using a fixed depth assumption, the system dynamically adjusts depth relationships based on whether the hand is detected in front of or behind the arm, resolving the drift error by adapting to varying spatial configurations.
2Adaptability or versatility
If depth maps are used for hand tracking, then three-dimensional information is available, but the system is sensitive to background variations and produces false positives
Solution Approach 1:
The patent segments the depth map into foreground (hand/arm) and background regions using a background-invariant decision forest. This segmentation isolates the hand from background variations, allowing the system to utilize depth information for accurate hand tracking while filtering out false positives caused by background changes.
Solution Approach 2:
The background-invariant decision forest acts as an intermediary between the raw depth map and hand detection. It processes the depth information to distinguish hand regions from background regions, mediating the effect of background variations and enabling robust hand tracking despite environmental changes.
3Device complexity
If unidirectional tracking is used, then the tracking algorithm is simple, but it cannot track hands when they move behind the arm
Solution Approach 1:
The patent transforms the static unidirectional tracking into a dynamic bidirectional tracking system. The algorithm adaptively determines the direction of hand movement relative to the arm and adjusts its detection strategy accordingly, enabling it to track hands whether they move in front of or behind the arm while maintaining reasonable computational complexity.
Data Source
AI summary
In the field of Human-computer interaction (HCI), i.e., the study of the interfaces between people (i.e., users) and computers, understanding the intentions and desires of how the user wishes to interact with the computer is a very important problem. The ability to understand human gestures, and, in particular, hand gestures, as they relate to HCI, is a very important aspect in understanding the intentions and desires of the user in a wide variety of applications. In this disclosure, a novel system and method for three-dimensional hand tracking using depth sequences is described. Some of the major contributions of the hand tracking system described herein include: 1.) a robust hand detector that is invariant to scene background changes; 2.) a bi-directional tracking algorithm that prevents detected hands from always drifting closer to the front of the scene (i.e., forward along the z-axis of the scene); and 3.) various hand verification heuristics.