Object Tracker Adaptive Motion Model Switching
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Solution Overview
Problem
Conventional particle filters for visual tracking are ineffective when the motion of an object cannot be fully represented by a preconfigured motion model, leading to poor tracking performance due to the reliance on a single motion model and the impracticality of switching between multiple models requiring learning of switching timing.
Innovation Solution
An object tracker that generates particles based on an estimated existence probability distribution in a video stream, using multiple motion models to observe likelihoods and choose the most suitable model for tracking, allowing adaptive and efficient tracking of objects with changing motion characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single motion model is used for tracking, then the device complexity is reduced, but the tracking accuracy deteriorates when the object motion cannot be fully represented by the preconfigured motion model
Solution Approach 1:
The system dynamically switches between multiple motion models based on the observed object motion characteristics. Instead of using a fixed single motion model, the tracker adapts by selecting the most appropriate motion model from multiple candidates, allowing the system to handle diverse motion patterns while maintaining reasonable complexity through on-demand model switching rather than maintaining all models simultaneously.
Solution Approach 2:
The system changes the motion model parameters by selecting different motion models from a predefined set based on the current tracking context. This allows the tracker to adjust its behavior to match the actual motion characteristics of the target object, improving accuracy without requiring a completely new system architecture.
2Measurement precision
If multiple motion models are used for tracking, then the tracking accuracy improves for objects with varying motion characteristics, but the device complexity increases
Solution Approach 1:
The system employs dynamic model selection where the complexity of using multiple motion models is managed through runtime switching based on observed motion characteristics. The tracker evaluates which motion model best fits the current object behavior and activates only that model, thereby achieving high tracking accuracy for diverse motion patterns while keeping the effective system complexity low at any given moment.
3Adaptability or versatility
If switching between multiple motion models is implemented, then the adaptability to different motion characteristics improves, but the ease of operation deteriorates due to the requirement of learning switching timing
Solution Approach 1:
The system performs self-service by automatically determining the appropriate switching timing between motion models based on observed object characteristics. The tracker independently evaluates which motion model is most suitable for the current situation and switches models autonomously without requiring external intervention or complex learning processes, thereby improving adaptability while maintaining ease of operation.
4Adaptability or versatility
If learning-based switching timing determination is used, then the adaptability improves, but the loss of time increases due to the learning process requirement
Solution Approach 1:
The system performs preliminary action by pre-defining multiple motion models and their applicable scenarios before actual tracking begins. This allows the tracker to quickly select the appropriate motion model based on pre-established criteria without requiring time-consuming learning processes during operation, thereby achieving high adaptability while minimizing time loss.
Solution Approach 2:
The tracker independently determines the optimal motion model selection based on pre-configured criteria and observed object characteristics, eliminating the need for external learning processes. This self-service approach enables rapid adaptation to different motion patterns without the time penalty associated with learning-based switching mechanisms.
Data Source
AI summary
A tracking start-and-stop determining unit of a tracking processing unit determines whether to start or stop tracking based on a predetermined condition. During tracking, a sampling unit 29 creates or eliminates particles using a probability density distribution estimated for the previous image frame. A first to nth model tracking units transit the particles according to motion models respectively assigned, and observe the likelihood of a candidate curve corresponding to each particle with respect to an edge image generated by an image processing unit. A tracking integration unit determines a motion model which causes the highest likelihood, and estimates a probability density distribution using an observation likelihood based on the motion model.