Bayesian Motion Modeling for Constrained Target Tracking
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Solution Overview
Problem
Conventional video analysis algorithms for surveillance systems assume incorrect kinematic and observation models for targets in environments with strong constraints, leading to misleading tracking results, particularly in scenarios like point of sale surveillance where motion patterns are specific and constrained.
Innovation Solution
The method employs Bayesian motion modeling with nonparametric probability density models to capture position-dependent prior knowledge and constraints, using multidimensional matrices trained on data sets to estimate target locations by maximizing joint probability estimates in each frame, considering both current and past target locations and observations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Kalman filtering or particle filtering algorithms are used for target tracking, then the system can provide basic target location estimation, but the tracking accuracy deteriorates in constrained environments due to incorrect kinematic and observation models
Solution Approach 1:
The patent changes the fundamental parameters of the tracking model by transitioning from conventional linear/Gaussian assumptions to nonparametric probability density models. This allows the system to adapt to constrained environments (like POS surveillance) where targets follow specific motion patterns, thereby improving both measurement precision and reliability simultaneously.
Solution Approach 2:
The patent introduces dynamic adaptation through nonparametric models that can learn from data and adjust to changing constraints in real-time. The system dynamically updates probability density models based on observed target behavior, enabling accurate tracking even when environmental constraints change or are not pre-programmed.
2Measurement precision
If nonparametric probability density models are used to capture position-dependent prior knowledge, then tracking accuracy improves in constrained environments, but computational complexity increases due to multidimensional matrix operations
Solution Approach 1:
The patent segments the complex probability density model into manageable components: position-dependent prior knowledge is separated from observation models, and multidimensional matrices are decomposed into smaller sub-matrices that can be processed independently. This segmentation reduces computational burden while preserving the accuracy benefits of nonparametric modeling.
Solution Approach 2:
The patent applies partial modeling by focusing computational resources only on the most critical dimensions and constraints. Rather than modeling all possible motion patterns equally, the system identifies and models only the significant position-dependent constraints relevant to the specific environment, reducing unnecessary computational complexity.
3Reliability
If position-dependent prior knowledge is incorporated through Bayesian motion modeling, then tracking reliability improves, but the system complexity increases due to need for training data and model learning
Solution Approach 1:
The patent performs preliminary model training during an offline phase, where the system learns position-dependent prior knowledge from training data before actual operation. This preliminary action stores the learned knowledge in pre-computed probability density models, allowing the online tracking system to simply query these pre-learned models without complex real-time learning, thus improving reliability while managing complexity.
Solution Approach 2:
The patent creates a simplified copy of the complex learning process by pre-computing and storing probability density models during training. During actual tracking operations, the system uses these pre-computed models as templates, copying the learned patterns rather than re-learning them in real-time. This copying approach maintains high reliability while significantly reducing operational system complexity.
Data Source
AI summary
A system and method includes obtaining and storing video frames from a series of video frames on a computer readable storage device, calculating probability estimates for target locations in each frame for targets in a constrained environment, and determining candidate target locations in each frame.