Digital Neuromorphic Vision System for Real-Time Object Tracking
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Solution Overview
Problem
Conventional image processing systems face challenges in efficiently analyzing high frame rate video data due to overwhelming computational demands, as they process all frames without prioritizing changes, leading to limited ability in real-time object detection and tracking.
Innovation Solution
A digital Neuromorphic (NM) vision system that uses a digital retina and engine to generate encoded image data by capturing differences and spatio-temporal regions, enabling improved object detection, classification, and tracking through feature extraction and post-processing operations like velocity vector generation and image segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing systems process all frames without prioritizing changes, then complete image data is processed, but computational demands become overwhelming and processing speed decreases
Solution Approach 1:
The system extracts only the essential changes from video frames by generating difference images that highlight moving objects while removing static background information. This extraction principle reduces the data volume significantly while preserving the critical information needed for object detection and tracking, thereby improving processing speed without sacrificing detection accuracy.
Solution Approach 2:
The system transforms the video data representation by changing from full-frame processing to difference image processing. This parameter change in data representation focuses computational resources on regions with temporal changes, enabling faster processing while maintaining effective object detection through targeted analysis of motion-related features.
2Reliability
If all video frames are processed in real-time, then complete object information is captured, but computational resources are overwhelmed
Solution Approach 1:
The system extracts only the moving object information from each frame by computing differences between consecutive frames. This removes the static background data that consumes computational resources without providing useful tracking information, thereby reducing energy consumption while maintaining reliable object tracking through focused processing of change-containing regions.
Solution Approach 2:
Instead of processing complete frames, the system applies partial processing only to regions where changes are detected. This selective action processes only the necessary portions of the video data for tracking purposes, significantly reducing computational resource consumption while maintaining sufficient tracking reliability through targeted analysis of motion regions.
3Quantity of substance
If high framerate video data is compressed by capturing differences between frames, then data volume is reduced, but processing complexity increases
Solution Approach 1:
The difference image processing naturally segments the video data into static background regions and dynamic object regions. This segmentation reduces data volume by representing only the changing portions, and the segmented structure actually simplifies subsequent processing by focusing computational attention on discrete object regions rather than entire frames, thereby reducing overall processing complexity.
Solution Approach 2:
Instead of compressing video data by reducing temporal resolution or applying complex compression algorithms, the system inverts the approach by transforming the data representation to difference images. This inversion captures only the essential information (changes) rather than attempting to compress all data, reducing data volume while the simplified difference representation actually decreases processing complexity compared to handling full-resolution frames.
Data Source
AI summary
A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.