Motion Estimation Using Event Density Thresholds
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Solution Overview
Problem
Existing motion estimation techniques for video streams are complex and costly due to the processing of entire frames, which includes redundant information, and struggle to efficiently capture scenes with varying brightness levels, leading to resource wastage and high computational complexity.
Innovation Solution
A method and system for motion estimation that segments frames into blocks, determines an event density factor for each block, compares it with a threshold value, estimates motion vectors based on the comparison, and processes blocks only where the event density factor exceeds the threshold, generating an event density map and block-level segmentation map to optimize block size for motion estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If entire frames are processed for motion estimation, then motion accuracy is maintained, but computational complexity and resource consumption increase significantly
Solution Approach 1:
The patent divides frames into multiple blocks and processes only those blocks where motion is detected, rather than processing entire frames. This segmentation approach maintains motion estimation accuracy in active regions while significantly reducing computational complexity in static regions.
Solution Approach 2:
The patent extracts and processes only the relevant portions of frames (blocks with motion events) while discarding or skipping static blocks. This extraction principle eliminates redundant processing of unchanged regions, reducing resource consumption while preserving motion detection accuracy where needed.
2Duration of action of stationary object
If fixed frame rate processing is used, then temporal coverage is uniform, but redundant information is transmitted wasting memory and energy
Solution Approach 1:
The patent uses event-based sensors that operate asynchronously, triggering processing only periodically when changes occur rather than at fixed intervals. This allows the system to maintain temporal coverage of important events while reducing energy consumption by avoiding processing during static periods.
Solution Approach 2:
The patent discards redundant frame data where no motion occurs and only retains and processes blocks with detected changes. This selective retention strategy reduces memory usage and energy consumption while preserving all meaningful temporal information.
3Measurement precision
If block matching is performed with all blocks of reference frame, then motion estimation precision improves, but processing resources required become prohibitively high
Solution Approach 1:
The patent extracts only the necessary blocks from reference frames for comparison - specifically those blocks corresponding to regions with detected motion events. This extraction eliminates the need to compare against all reference frame blocks, reducing processing resources while maintaining precision for active regions.
Solution Approach 2:
The patent performs partial block matching only where needed (in regions with motion events) rather than exhaustively matching all blocks. This partial action approach provides sufficient motion estimation precision for dynamic regions while avoiding the excessive resource consumption of complete frame-wide matching.
Data Source
AI summary
Provided is a method of motion estimation for processing a video stream comprising a plurality of frames, the method including segmenting at least one frame, from among the plurality of frames, into a plurality of blocks, determining an event density factor for each block included in a frame, wherein the event density factor of the block corresponds to a number of events accumulated in the block across frames in a predetermined time duration, comparing the determined event density factor with a threshold value, estimating a motion vector of the block based on the comparison, and processing the block in the video stream based on the estimated motion vector of the block.