Generative Model for Scene-Invariant Behavior Recognition
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Solution Overview
Problem
Existing behavior recognition models assume known scene and camera types, making it impossible to handle test trajectories from unknown scenes or camera types.
Innovation Solution
An information processing apparatus that acquires trajectory data, assigns groups to the data, and generates generative models using common time-sequences of velocity transformations, allowing for scene and camera invariant behavior recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If behavior recognition models assume known scene and camera types, then modeling precision is improved, but adaptability to unknown scenes deteriorates
Solution Approach 1:
The patent transforms the behavior recognition approach by changing from scene-specific parameters to scene-invariant parameters. Instead of modeling trajectories based on known scene and camera types, the invention uses velocity transformations and motion primitives that are independent of specific scene characteristics, allowing the same model to work across unknown scenes while maintaining recognition precision
Solution Approach 2:
The patent creates a universal behavior recognition model that functions across multiple scenes and camera types without requiring scene-specific calibration. By using group-specific generative variables and scene-invariant motion primitives, a single model can handle diverse scenarios including unknown scenes, eliminating the need for separate models for each scene type
2Measurement precision
If scene-specific generative models are used, then behavior recognition accuracy is improved, but device complexity increases due to need for multiple models
Solution Approach 1:
The patent reduces model complexity by creating a universal behavior recognition system that handles multiple scenes with a single framework. Instead of maintaining separate generative models for each scene, the invention uses one generative model with group-specific variables that can adapt to different scenes, significantly reducing the number of models needed while maintaining recognition accuracy
Solution Approach 2:
The patent segments the behavior recognition process into group-specific generative variable estimation and scene-invariant motion primitive application. This segmentation allows the system to handle scene diversity through variable estimation while using a common set of motion primitives, reducing overall model complexity compared to creating entirely separate models for each scene
3Reliability
If calibration and training data are required for each scene, then behavior recognition reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The patent enables the behavior recognition system to automatically adapt to new scenes without requiring manual calibration or scene-specific training data. The group-specific generative variables are estimated automatically from the input trajectories, and the scene-invariant motion primitives self-adjust to work with unknown scenes, making the system self-sufficient and easy to deploy in new environments
Solution Approach 2:
The patent performs preliminary grouping of trajectories based on motion patterns before applying the generative model. By organizing trajectories into groups with similar behavior patterns and estimating group-specific variables in advance, the system prepares the data structure needed for reliable recognition without requiring scene-specific calibration procedures
Data Source
AI summary
The information processing apparatus (2000) of the example embodiment 1 includes an acquisition unit (2020), a modeling unit (2040), an output unit (2080). The acquisition unit (2020) acquires a plurality of trajectory data. The trajectory data represents a time-sequence of observed positions of an object. The modeling unit (2040) assigns one of groups for each trajectory data. The modeling unit (2040) generates a generative model for each group. The generative model represents trajectories assigned to the corresponding group by a common time-sequence of velocity transformations. The velocity transformation represents a transformation of velocity of the object from a previous time frame, and is represented using a set of motion primitives defined in common for all groups. The output unit (2060) outputs the generated generative models.