Motion Detection Computing Device Using Vector Posture Matching
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Solution Overview
Problem
Current motion capture technologies face challenges in accurately detecting feature points and determining the motion of an object of interest, particularly in varying lighting conditions and complex postures, which affects the quality of computer-generated character rendering in applications like interactive video games.
Innovation Solution
A computing device and method that utilize a vector forming unit, posture identifying unit, motion similarity unit, and motion identifying unit to form vectors from feature points, match postures in a database, and identify predetermined motions based on these matches, enabling precise detection and rendering of object motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional motion capture methods are used to detect feature points, then the system can track object motion, but the detection accuracy deteriorates in varying lighting conditions and complex postures
Solution Approach 1:
The patent replaces traditional optical-mechanical motion capture systems with a computational approach using vector mathematics and database matching. Instead of relying on physical markers and complex optical tracking that fail in varying lighting, the system uses feature point coordinate extraction, vector formation, and posture database comparison to achieve robust motion detection independent of lighting conditions.
Solution Approach 2:
The patent transforms the motion detection problem from direct image analysis to parameter-based vector comparison. By extracting feature point coordinates, forming vectors from these points, and comparing vector sets against a database of predetermined postures, the system changes the detection parameters from pixel-intensity-based (lighting-dependent) to geometric-coordinate-based (lighting-independent).
2Measurement precision
If complex algorithms are used to improve motion detection accuracy, then feature point detection improves, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-establishing a database of vectors corresponding to predetermined postures before actual motion detection. During runtime, the system only needs to extract current feature points, form vectors, and match against the pre-computed database, avoiding complex real-time calculations. This shifts computational complexity from the detection phase to the setup phase.
Solution Approach 2:
The patent uses copying by creating a database of predetermined posture vectors that replicates possible object configurations. Instead of calculating motion from scratch for each frame, the system copies matching posture data from the database and compares vector sets, significantly reducing computational complexity while maintaining detection accuracy.
3Measurement precision
If more feature points are tracked to improve motion accuracy, then motion detection precision improves, but the quantity of data to be processed increases
Solution Approach 1:
The patent applies extraction by selecting only the essential feature points that define object posture and motion, rather than processing all pixels or all detectable points. By extracting key feature point coordinates and forming vectors from these selected points, the system reduces data volume while maintaining sufficient information for accurate motion detection and posture identification.
Data Source
AI summary
A computing device for motion detection in a system capable of detecting feature points of an object of interest is disclosed. The computing device includes a vector forming unit to form a plurality of vectors associated with a set of the feature points and form a vector set based on the vectors, a posture identifying unit to identify a match of a posture in a database based on the vector set, a motion similarity unit to identify a set of predetermined postures in the database based on the matched posture and an immediately previous matched posture, and a motion identifying unit to identify a predetermined motion in the database based on the set of predetermined postures.