Motion Field Prediction for Dynamic Scene Evolution
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Solution Overview
Problem
Understanding and predicting the evolution of complex dynamic scenes, such as in team sports, is challenging due to the involvement of both local and global structural movements of multiple objects, making it difficult to infer future events from video footage or positioning data.
Innovation Solution
A method that involves accessing active object position data, extracting individual object motions, constructing a motion field, and using it to predict points of convergence at future spatial locations by analyzing ground-level motions and generating a dense flow field from sparse data, which can be applied to various dynamic scenes beyond sports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If only video footage or positioning data is used, then data collection is simple, but understanding the overall development of the scene and predicting future events is difficult
Solution Approach 1:
The patent introduces motion fields as an intermediary representation that bridges raw positioning data and scene understanding. Motion fields aggregate individual object motions into a unified flow representation, enabling prediction of scene evolution without requiring complex direct analysis of all object interactions. This intermediary layer transforms sparse positioning data into dense motion flow information that reveals global scene dynamics.
Solution Approach 2:
The system uses the motion information of objects themselves to predict their future behavior and scene evolution. By constructing motion fields from observed object motions, the system enables objects to 'predict' their own trajectories and convergence points without external intervention. The motion field automatically captures both local object behaviors and global structural movements.
2Loss of information
If individual object motions are tracked separately, then local behaviors are captured, but global structural movements are missed
Solution Approach 1:
The patent merges individual object motions into a unified motion field representation. By combining velocity vectors of multiple objects at different spatial locations, the system creates a dense flow field that simultaneously captures local object behaviors and global structural movements. This merging process transforms separate tracking data into integrated scene-level understanding.
Solution Approach 2:
The system transitions from tracking individual objects in 2D space to representing motions as a 3D velocity field (x, y, v). By adding the velocity dimension to spatial positioning, the motion field captures both where objects are and how they are moving, enabling prediction of future positions and convergence points while maintaining information about global scene structure.
3Measurement precision
If dense flow field is generated from sparse data, then prediction accuracy is improved, but computational requirements increase
Solution Approach 1:
The system generates dense flow fields by interpolating motion information at locations where no objects are present, using data from nearby objects. This partial action approach creates comprehensive coverage without requiring direct measurement at every point, achieving prediction accuracy through selective sampling and interpolation rather than exhaustive data collection.
Solution Approach 2:
The motion field construction copies and propagates motion patterns from observed objects to surrounding spatial locations. By replicating velocity field information from object positions to grid points in the scene, the system creates a dense representation that preserves the statistical properties of object motions while enabling predictions in regions without direct observations.
Data Source
AI summary
Described herein are methods, systems, apparatuses and products for utilizing motion fields to predict evolution in dynamic scenes. One aspect provides for accessing active object position data including positioning information of a plurality of individual active objects; extracting a plurality of individual active object motions from the active object position data; constructing a motion field using the plurality of individual active object motions; and using the motion field to predict one or more points of convergence at one or more spatial locations that active objects are proceeding towards at a future point in time. Other embodiments are disclosed.