Digital Neuromorphic Vision System for Real-Time Object Detection
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Solution Overview
Problem
Conventional image processing systems face challenges in efficiently analyzing high frame rate video data, leading to computational overload due to the need to process large amounts of redundant data, which limits real-time image utilization and accuracy in object detection and tracking.
Innovation Solution
A digital neuromorphic vision system that uses CMOS technology and a digital retina to simulate analog neuromorphic system functionality, focusing on feature extraction and data compression by capturing differences between frames, thereby reducing processing burden and enhancing object detection and tracking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image processing systems process high frame rate video data, then complete image data is available for analysis, but computational overload occurs due to large amounts of redundant data
Solution Approach 1:
The patent extracts only the essential features from complete image data using feature extraction algorithms. Instead of processing all pixel data, the system identifies and processes only relevant features such as edges, corners, and motion vectors, thereby reducing computational load while maintaining detection accuracy.
Solution Approach 2:
The patent segments the image processing task into multiple stages: feature detection, feature description, and feature matching. This segmentation allows the system to process different aspects of image data separately and efficiently, avoiding the need to process all data simultaneously and reducing overall computational complexity.
2Measurement precision
If all image data is processed for object detection, then detection accuracy is maintained, but processing time increases reducing real-time utilization
Solution Approach 1:
The patent applies partial action by processing only a subset of image data that contains relevant features for object detection. The system identifies regions of interest and processes only those areas, rather than analyzing every pixel in the image, thereby reducing processing time while maintaining detection precision.
Solution Approach 2:
The patent performs preliminary feature extraction and filtering before main object detection processing. By pre-identifying potential objects of interest and extracting their key features in advance, the system reduces the amount of data requiring detailed analysis, thus decreasing overall processing time while preserving detection accuracy.
3Productivity
If high framerate video data is compressed by capturing frame differences, then data processing burden is reduced, but some image information is lost
Solution Approach 1:
The patent applies local quality by differentiating the processing approach for different regions of the image. Static background regions are processed with minimal computation while regions containing motion or objects of interest receive more detailed processing. This ensures that information loss in compressed regions does not compromise overall detection accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where the results of preliminary compression and feature extraction are evaluated, and processing parameters are adjusted accordingly. If important information is detected to be lost in compression, the system adapts by allocating more processing resources to those specific regions, thereby maintaining information integrity where needed.
Data Source
AI summary
A system and methodologies for neuromorphic (NM) vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.