Context-Conditioned Motion Models for Multi-Player Trajectory Tracking
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Solution Overview
Problem
Conventional multi-target tracking algorithms, such as those used in team sports and pedestrian tracking, face challenges in accurately modeling complex interactions between objects, leading to inaccurate trajectory predictions due to simplifications like independent motion models and NP-hard data association problems.
Innovation Solution
A system and method that utilize data association with game context features and random decision forests to condition motion models on global state vectors, enabling the generation of high-level trajectories by implicitly encoding complex inter-object correlations, thereby maintaining tractability and capturing the influence of one player's motion on another.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If independent motion models are used to track each object separately, then the inference problem remains computationally tractable, but the tracking accuracy deteriorates because object motions are not independent
Solution Approach 1:
The patent segments the complex multi-object tracking problem into individual object tracking subproblems, where each object is tracked independently using its own motion model. This segmentation maintains computational tractability while the system later integrates results through data association to improve overall tracking accuracy.
Solution Approach 2:
The patent changes the parameters of the motion models to better fit specific object types (e.g., different motion characteristics for players vs. ball). By adjusting motion model parameters rather than complexity, the system maintains tractability while improving accuracy for each object class.
2Measurement precision
If complex inter-object correlation models are used to accurately describe all possible interactions between objects, then the tracking accuracy improves, but the device complexity increases significantly
Solution Approach 1:
The patent extracts and removes the complex inter-object correlation modeling from the system, instead relying on independent motion models combined with data association. This extraction eliminates the computational burden of modeling all pairwise interactions while maintaining reasonable tracking accuracy through alternative means.
Solution Approach 2:
The patent introduces data association as an intermediary mechanism that connects individual object tracks without requiring direct modeling of inter-object correlations. This mediator enables the system to capture indirect relationships through shared observation data while avoiding the complexity of explicit correlation models.
3Stability of the object's composition
If data association methods are used to link tracklets across frames, then the tracking continuity improves, but the association problem becomes NP-difficult when complex affinity models are used
Solution Approach 1:
The patent segments the data association problem into frame-by-frame linkings rather than global optimization across all frames. By solving the association problem locally at each frame transition and chaining results together, the system maintains tracking continuity while avoiding NP-difficulty associated with global optimization.
Solution Approach 2:
The patent applies partial action by using simplified affinity models for data association rather than comprehensive complex models. This partial approach focuses on the most critical association cues (spatial proximity, motion consistency) while ignoring less important factors, thereby maintaining tractability while achieving sufficient tracking continuity.
Data Source
AI summary
A method and device generates a trajectory. The method includes receiving a plurality of tracklets indicative of movement of a plurality of targets over a predetermined temporal interval. The method includes determining a plurality of context data for a pair of tracklets based upon at least one additional tracklet. The method includes computing a probability that the pair of tracklets relate to a first one of the targets. The method includes generating a trajectory for the first target based upon a concatenation of select ones of the tracklets. The concatenation maximizes the probability that the pair of tracklets correspond to the first target based upon the context data associated with the pair of the tracklets.