Multi-hypothesis Moving Object Detection Using Tree-Structured Signal Analysis
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Solution Overview
Problem
Current object detection systems face challenges in accurately detecting moving objects with low signal-to-noise ratios, particularly when the object is dim or has a non-constant trajectory, leading to higher false alarm rates and missed detections.
Innovation Solution
A method and apparatus that utilize a tree structure with hierarchies of nodes to analyze a sequence of images, identifying potential positions and tracks of objects over time, selecting established tracks based on signal-to-noise ratios, and reporting object detection, while also tracking object movement without assuming a constant trajectory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a lower detection threshold is used to detect dim objects, then detection sensitivity is improved, but false alarm rate increases
Solution Approach 1:
The patent combines multiple image frames through integration to enhance the signal-to-noise ratio of dim moving objects. By merging information across multiple frames, the system improves detection sensitivity for low-contrast objects while maintaining reliability through the cumulative signal enhancement rather than relying on lower single-frame thresholds
Solution Approach 2:
The system dynamically adjusts detection parameters based on observed object motion patterns. By tracking object trajectories and adapting to non-constant velocity movements, the system maintains accurate detection of dim objects without triggering false alarms from stationary or randomly moving clutter
2Measurement precision
If track before detect techniques are used to enhance signal-to-noise ratio, then detection capability for low-SNR objects is improved, but detection accuracy decreases for objects with non-constant trajectory
Solution Approach 1:
The system employs dynamic trajectory modeling that adapts to changing object velocities and acceleration patterns. Unlike static track-before-detect methods that assume constant velocity, this system updates motion parameters continuously based on observed object behavior, maintaining both high signal-to-noise ratio enhancement and accuracy for maneuvering targets
Solution Approach 2:
The detection process is segmented into distinct phases: initial detection using multiple frame integration, trajectory estimation, and continuous tracking with adaptive parameter updates. This segmentation allows the system to apply different processing strategies optimized for each stage, improving overall detection accuracy while maintaining signal-to-noise ratio enhancement
Data Source
AI summary
A method and apparatus for analyzing a sequence of images. Signal-to-noise ratios are identified for potential tracks using a tree having hierarchies of nodes identifying potential positions for an object over a period of time and using the sequence of images. Each hierarchy in the hierarchies of nodes represents a time and the potential positions in the tree form the potential tracks for the object. A potential track is selected from the potential tracks as an established track for the object using the signal-to-noise ratios, and a detection of the object is reported.