Motion Detection Feature Descriptor Using Position and Gradient
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Solution Overview
Problem
Conventional gradient-based feature descriptors, such as SIFT, SURF, and HOG, often suffer from poor Signal-to-Noise Ratio (SNR) in smooth areas, leading to increased false detections, especially in dark or dim scenes with translational movement.
Innovation Solution
The proposed solution involves defining feature descriptors for motion-detection regions by incorporating both pixel-value gradients and position information, allowing for the calculation of feature-descriptor values that account for both gradient magnitudes and positions within the region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional gradient-based feature descriptors (SIFT, SURF, HOG) are used for motion detection, then the system can detect motion in video frames, but the system produces increased false detections in smooth areas with poor SNR, especially in dark or dim scenes
Solution Approach 1:
The patent combines multiple feature types (gradient-based features, raw pixel intensity differences, and position information) into a unified motion detection approach. By merging these different feature sources, the system compensates for the weaknesses of individual descriptors in smooth areas, reducing false detections while maintaining motion detection capability.
Solution Approach 2:
The patent creates a composite feature representation by integrating gradient magnitudes, gradient directions, raw pixel intensity differences, and position coordinates into a single comprehensive feature vector. This composite approach类似于 using composite materials, where the strengths of different feature types complement each other to overcome the poor SNR problem in smooth regions.
2Difficulty of detecting and measuring
If gradient-based feature descriptors are used in dark or dim scenes with translational movement, then motion can be detected, but false positives increase due to poor SNR in smooth areas
Solution Approach 1:
The patent applies different feature computation strategies to different regions of the image. In smooth areas where gradient-based descriptors fail, the system relies more on raw pixel intensity differences and position information. In regions with edges or textures, gradient-based features remain effective. This local adaptation reduces false positives while maintaining motion detection capability across various scene conditions.
Solution Approach 2:
The patent introduces position information and raw pixel intensity differences as intermediary features that bridge the gap in smooth areas where traditional gradient descriptors fail. These intermediary features provide additional information that helps distinguish true motion from noise in dark or dim scenes, reducing false detections.
3Reliability
If position information and gradient values are incorporated into feature descriptors, then false detections are reduced in low lighting conditions, but the computational complexity of the motion detection system increases
Solution Approach 1:
The patent divides the motion detection process into separate computational stages: gradient computation, position information extraction, raw pixel difference calculation, and feature vector assembly. This segmentation allows each component to be optimized independently and enables parallel processing, reducing the overall computational burden while maintaining the comprehensive feature representation that reduces false detections.
Data Source
AI summary
A motion-detection method includes for a motion-detection region in a first image of a scene, (i) determining a respective first pixel-value gradient at each of a plurality of pixel-coordinates within the motion-detection region, and (ii) determining a first feature-descriptor value as a function of the first pixel-value gradients and position vectors defined by the plurality of pixel-coordinates. The method also includes, for the motion-detection region in a second image of the scene, (i) determining a respective second pixel-value gradient at each of the plurality of pixel-coordinates, and (ii) determining a second feature-descriptor value as a function of the second pixel-value gradients and the position vectors. The method also includes determining a difference-parameter that varies in direct relationship to a difference between the first feature-descriptor value and the second feature-descriptor value.