Sparse Optical Flow Estimation via Pixel Masking
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Solution Overview
Problem
Existing optical flow estimation methods require significant computational resources and time due to the need for dense optical flow maps, leading to high latency, power consumption, and memory usage, while also suffering from degraded accuracy due to uniform processing across all pixels and frames.
Innovation Solution
The method performs sparse optical flow estimation by determining a subset of pixels using techniques like differential and attentional field masking, focusing on regions with significant motion, and using machine learning algorithms to select relevant pixels, thereby generating optical flow maps for fewer pixels with reduced latency and computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense optical flow maps are generated for all pixels, then measurement precision is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the optical flow estimation problem by dividing pixels into different groups based on their motion characteristics and importance. Instead of processing all pixels uniformly, the system identifies and processes only significant pixels (those with meaningful motion) while skipping redundant pixels, thereby reducing computational complexity while maintaining estimation accuracy for critical regions
Solution Approach 2:
The patent applies local quality by using different processing strategies for different regions of the image. Significant pixels (those indicating important motion) receive detailed processing, while non-significant pixels are either skipped or processed with reduced detail. This allows the system to maintain high measurement precision for critical areas while reducing overall computational burden
2Measurement precision
If dense optical flow maps are generated for all pixels, then measurement precision is improved, but processing time increases leading to high latency
Solution Approach 1:
The patent extracts only the essential information needed for accurate optical flow estimation by identifying and processing only significant pixels. By taking out the redundant processing of non-critical pixels and focusing computational resources on pixels that actually contribute to motion detection accuracy, the system reduces processing time and latency while maintaining measurement precision
Solution Approach 2:
The patent applies partial action by performing optical flow estimation on only a subset of pixels rather than all pixels. By selectively processing only the necessary pixels (those with significant motion characteristics), the system achieves sufficient measurement precision without the excessive processing time required for complete dense optical flow maps
3Measurement precision
If dense optical flow maps are generated for all pixels, then measurement precision is improved, but power consumption increases
Solution Approach 1:
The patent segments the pixel set into significant and non-significant categories, processing only the former with full computational resources. This segmentation allows the system to maintain measurement precision for important motion detection while avoiding the excessive power consumption that would result from processing all pixels uniformly
Solution Approach 2:
The patent applies local quality by concentrating computational power and energy on specific regions containing significant pixels rather than distributing energy uniformly across the entire image. This localized processing approach maintains accuracy where needed while reducing overall power consumption
4Measurement precision
If uniform processing is applied to all pixels, then measurement precision is maintained, but device complexity increases
Solution Approach 1:
The patent introduces dynamics by making the processing approach adaptive rather than static. The system dynamically determines which pixels are significant based on their motion characteristics and processes them accordingly. This dynamic adaptation allows the system to maintain measurement precision while reducing processing complexity by avoiding uniform processing of all pixels
Data Source
AI summary
Systems and techniques are described herein for performing optical flow estimation between one or more frames. For example, a process can include determining a subset of pixels of at least one of a first frame and a second frame, and generating a mask indicating the subset of pixels. The process can include determining, based on the mask, one or more features associated with the subset of pixels of at least the first frame and the second frame. The process can include determining optical flow vectors between the subset of pixels of the first frame and corresponding pixels of a second frame. The process can include generating an optical flow map for the second frame using the optical flow vectors.