Automated Multiple Target Detection via Motion Compensation
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Solution Overview
Problem
Conventional radar systems are inadequate for detecting and tracking multiple targets due to limitations in providing bearing information and feature data, which are essential for accurate identification and tracking, especially in scenarios involving moving and stationary objects.
Innovation Solution
A vision-based automated multiple target detection and tracking system that uses video data from cameras mounted on platforms, compensates for platform motion, and employs algorithms like recursive random sample consensus (R-RANSAC) and Kalman filters to generate and update motion models, enabling robust tracking of moving and stationary targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar systems are used for detection and tracking, then detection capability is provided, but bearing information and feature data are insufficient for accurate identification and tracking
Solution Approach 1:
The patent combines radar detection capabilities with visual detection capabilities into an integrated system. The visual detection component provides bearing information and feature data that complements radar data, enabling accurate identification and tracking of multiple targets by merging information from both detection modalities.
2Measurement precision
If video data processing is used to provide bearing information and feature data, then target identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary processing of video data including motion compensation and noise removal before target detection. By preparing the video data in advance through these preprocessing steps, the computational complexity of subsequent target identification is reduced while maintaining high accuracy.
Solution Approach 2:
The video data processing is divided into separate functional modules: motion compensation module, noise removal module, and target detection module. This segmentation allows each module to be optimized independently and processed in sequence, reducing overall computational complexity while maintaining target identification accuracy.
3Measurement precision
If motion compensation and noise removal are applied to video data, then target detection accuracy is improved, but processing time increases
Solution Approach 1:
The system applies motion compensation and noise removal operations periodically at specific intervals during video data processing rather than continuously on every frame. This periodic application maintains target detection accuracy while significantly reducing the total processing time required.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively detects and tracks multiple targets by providing bearing information and detailed feature data, enhancing the ability to handle complex scenarios with moving and stationary objects, and operates in real-time with computational efficiency.
Implementation Method 1
movement data captured by an inertial measurement unit (IMU) coupled to the platform
Implementation Method 2
compensating for platform motion includes applying a geometric transformation to frames of the video data
Data Source
AI summary
For automated detection and tracking of multiple targets, an apparatus, method, and program product are disclosed. The apparatus includes a camera that captures video data and a processor that compensates for camera motion in the video data, processes the compensated video data to remove noise and spurious returns, detects one or more targets within the processed video data, and identifies target information for each target in the processed video data.