Object Detection Velocity Estimation Signal-to-Noise Ratio
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current object detection and tracking systems using velocity matched filters face challenges in real-time surveillance, particularly when objects are dim, small, or have unknown velocities, leading to high false alarm rates and resource-intensive processing.
Innovation Solution
A method and apparatus that analyze a sequence of images by identifying a region of interest, dividing it into sections, calculating signal-to-noise ratios for potential velocities, and selecting the velocity with the highest ratio to improve detection accuracy and reduce false alarms.
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 system performs preliminary velocity estimation and candidate generation before final detection. By pre-calculating velocity matched filters for multiple potential velocities and identifying candidate objects that match expected motion patterns, the system prepares detection criteria in advance, allowing it to distinguish true objects from noise more effectively at lower thresholds
Solution Approach 2:
The system uses feedback from velocity matched filter results to adjust detection decisions. By analyzing the strength and consistency of velocity match signals across multiple frames and velocities, the system can confirm or reject detections dynamically, reducing false alarms while maintaining sensitivity to dim objects
2Measurement precision
If velocity matched filters are applied to multiple images over time to detect low signal-to-noise ratio objects, then detection capability is improved, but processing time and computational resources increase
Solution Approach 1:
The system segments the detection process into distinct stages: velocity estimation, candidate generation, and confirmation. By dividing the processing into manageable segments that can be performed on different image frames independently, the system reduces the computational burden on any single frame while maintaining overall detection capability across the temporal sequence
Solution Approach 2:
The system applies velocity matched filters to a selected subset of potential velocities rather than exhaustively processing all possible velocities for every object candidate. By focusing computational effort on the most likely velocity ranges based on preliminary analysis, the system achieves sufficient detection performance with reduced processing time
3Measurement precision
If the number of objects is assumed to be known ahead of time for track before detect processing, then tracking accuracy is improved, but system adaptability decreases
Solution Approach 1:
The system dynamically adjusts the number of tracked objects and velocity parameters based on incoming data rather than relying on fixed pre-assumptions. By continuously estimating object count and velocity distributions from the image sequence itself, the system adapts to varying scene conditions while maintaining tracking accuracy through iterative refinement of its models
Data Source
AI summary
Methods and apparatuses for analyzing a sequence of images for an object are disclosed herein. In a general embodiment, the method identifies a region of interest in the sequence of images. The object is likely to move within the region of interest. The method divides the region of interest in the sequence of images into sections and calculates signal-to-noise ratios for a section in the sections. A signal-to-noise ratio for the section is calculated using the section in the image, a prior section in a prior image to the image, and a subsequent section in a subsequent image to the image. The signal-to-noise ratios are for potential velocities of the object in the section. The method also selects a velocity from the potential velocities for the object in the section using a potential velocity in the potential velocities having a highest signal-to-noise ratio in the signal-to-noise ratios.