Foreground Blob Detection for High-Resolution Video Object Analysis
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Solution Overview
Problem
High-resolution video data processing requires significant computational resources for real-time object detection, as the number of processing cycles increases linearly with image resolution, leading to inefficiencies in existing technologies.
Innovation Solution
The technique focuses on detecting foreground pixels and generating smaller image portions around suspected objects, reducing the area processed for object detection, which involves foreground extraction, blob formation, and subsequent object detection using neural networks to classify objects with reduced computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object detection is performed on the entire high-resolution image frame, then detection accuracy is maintained, but processing time increases linearly with image resolution
Solution Approach 1:
The patent divides the image processing task into two stages: first, simple foreground extraction is performed on the entire high-resolution frame to identify regions with pixel changes; second, complex object detection is applied only to cropped portions containing these foreground regions. This segmentation of processing complexity by spatial region resolves the contradiction by maintaining full-frame detection coverage while concentrating computational resources only where objects are likely to appear.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image: full-resolution object detection is applied locally only to regions containing foreground pixels, while background regions receive no object detection processing. This local quality approach maintains detection accuracy in relevant areas while eliminating wasteful processing in empty areas, thereby reducing overall processing time without sacrificing detection performance.
2Reliability
If the entire image frame is processed for object detection, then all objects are detected, but computational resources are wasted on empty regions
Solution Approach 1:
The patent performs preliminary foreground extraction on the entire image frame before performing object detection. This preliminary action identifies all regions that may contain objects, allowing subsequent object detection to be confined to these specific regions. This ensures detection completeness is maintained while dramatically improving processing efficiency by avoiding detection operations on empty regions.
Solution Approach 2:
The patent extracts and isolates only the relevant portions of the image (cropped regions containing foreground pixels) for object detection processing, separating them from the rest of the image. This extraction approach ensures that all potential objects are detected while eliminating wasteful processing of empty background regions, thereby resolving the contradiction between detection completeness and processing efficiency.
3Illumination intensity
If high-resolution video data is processed in real-time, then video quality is maintained, but processing power requirements become prohibitively high
Solution Approach 1:
The patent segments the processing pipeline into a low-power preprocessing stage (foreground extraction on full-resolution frames) and a high-power detection stage (applied only to cropped regions). This segmentation allows the system to maintain high video quality throughout while concentrating computational power only where needed, thereby reducing overall processing power requirements for real-time high-resolution video analysis.
Solution Approach 2:
The patent dynamically changes the processing parameter (image resolution/region size) based on content: full-resolution processing is applied only to regions containing foreground pixels, while background regions are processed at lower resolution or skipped entirely. This parameter change approach maintains video quality in relevant areas while reducing processing power consumption overall, enabling real-time processing of high-resolution video.
Data Source
AI summary
A method for use in processing of image data stream is described. The method comprising: providing input data comprising at least one sequence of image data pieces indicative of a region of interest. Processing one or more image frames for detection of one or more foreground pixels regions. Collecting adjacent foreground pixels and determining one or more blobs associated with one or more suspected foreground objects in the region of interest Selecting one or more blobs and generating image portion indicative of one or more suspected foreground objects. And processing said one or more image portions for detection of one or more foreground objects and corresponding one or more object classes. For generating output data comprising class probability for one or more objects detected in one or more frame images of said image data stream.