Motion Object Classification via Bayesian Feature Profiles
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Solution Overview
Problem
Existing methods for classifying objects in motion using multiple sensors and observations fail to adequately represent statistical dependencies due to high dimensionality and infinite possible trajectories, leading to sparse data sets and sub-optimal classification results.
Innovation Solution
The method involves providing feature data indexed by object class, orientation, and sensor, along with representative models for orientation motion profiles, acquired through multiple sensors and time instances, using Bayes' Rule to classify target objects based on posterior probabilities, generating reference feature vectors, and performing Bayesian classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If training data is generated offline over a subset of all possible trajectories and sensor observation times, then the data generation process is manageable, but the resulting data set becomes sparse and does not adequately represent the statistics for successful classification
Solution Approach 1:
The patent applies preliminary action by generating training data offline before actual target observation. Representative motion profiles are pre-computed for various object classes, and training datasets are constructed in advance covering multiple hypothetical trajectories and sensor observation times. This allows the classification system to be pre-trained on diverse scenarios, improving its ability to handle real-world variations without requiring exhaustive real-time data collection.
2Measurement precision
If the number of possible trajectories and observation times is considered, then the dimensionality of the joint feature space becomes large and unknown, but attempting to capture all dependencies becomes computationally infeasible
Solution Approach 1:
The patent segments the high-dimensional joint feature space by introducing motion profiles as intermediate representations. Instead of directly modeling all possible trajectory-combination features, the system divides the problem into: (1) object class identification, (2) motion profile matching, and (3) trajectory-specific feature extraction. This segmentation reduces the effective dimensionality by grouping similar trajectories under common motion profiles, making the classification task computationally tractable while preserving statistical dependencies.
Solution Approach 2:
The patent introduces motion profiles as an additional dimensional layer in the feature space. Rather than directly navigating the exponentially growing joint feature space of multiple sensors and trajectories, the system adds a motion profile dimension that organizes and structures the data. This dimensional transformation allows the system to capture temporal and spatial dependencies efficiently by representing them through standardized motion profile templates.
3Productivity
If observations are assumed to be independent, then the computational complexity is reduced, but the classification performance becomes sub-optimal
Solution Approach 1:
The patent introduces motion profiles as intermediary structures that capture dependencies between multiple sensor observations. Instead of directly modeling complex inter-observation dependencies, the system uses motion profiles as mediators that encode temporal and spatial relationships. Each observation is evaluated in the context of its corresponding motion profile, allowing the system to account for dependencies between observations while maintaining computational efficiency through the structured intermediate representation.
Data Source
AI summary
A method for classifying objects in motion that includes providing, to a processor, feature data for one or more classes of objects to be classified, wherein the feature data is indexed by object class, orientation, and sensor. The method also includes providing, to the processor, one or more representative models for characterizing one or more orientation motion profiles for the one or more classes of objects in motion. The method also include acquiring, via a processor, feature data for a target object in motion from multiple sensors and/or for multiple times and trajectory of the target object in motion to classify the target object based on the feature data, the one or more orientation motion profiles and the trajectory of the target object in motion.