Scene Recognition via Labeled Feature Vectors
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Solution Overview
Problem
Existing image recognition systems face latency issues and lack holistic scene-level analysis, making them impractical for real-time processing on mobile devices and inadequate for interpreting vehicular video streams.
Innovation Solution
An AI-based image processing system utilizing a deep neural network with probability gating for precise categorization, featuring a pre-processing unit that generates attention and background regions, and a processing unit with an encoder, labeler, and class re-grouper to produce distinctive feature vectors and parent labeled vectors, incorporating multi-mode sensor information for robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object recognition methods, pixel-level segmentation, or scanning detection window are used, then image recognition accuracy is improved, but processing time increases significantly causing latency
Solution Approach 1:
The image is divided into multiple patches that are processed independently and in parallel. Each patch is resized to a smaller dimension and processed separately through the neural network, enabling parallel computation while maintaining recognition accuracy through aggregation of patch-level predictions.
Solution Approach 2:
Instead of processing the entire high-resolution image at once, the system processes a subset of strategically selected patches. This partial processing approach reduces computational load and processing time while still capturing sufficient information for accurate scene recognition.
2Device complexity
If existing vehicular video systems provide only basic image overlay or storage without interpretation, then device complexity is reduced, but scene-level analysis capability is lost
Solution Approach 1:
A neural network processor acts as an intermediary between the image capture device and the output display. This intermediary component performs automated scene recognition and generates interpretable labels, providing holistic scene-level analysis without requiring complex manual processing or interpretation systems.
Solution Approach 2:
The system performs self-service by automatically analyzing and labeling scenes without requiring external interpretation or manual processing. The neural network autonomously processes images, generates scene predictions, and outputs results, eliminating the need for additional complex analysis systems.
3Measurement precision
If deep neural network with probability gating is used for precise categorization, then recognition accuracy is improved, but computational cost increases
Solution Approach 1:
The computational workload is segmented across multiple image patches processed in parallel. This distribution of computation reduces the computational cost per patch while maintaining overall recognition accuracy through aggregation of results from all patches.
Solution Approach 2:
The system applies partial processing by selecting and processing only the most relevant image patches rather than analyzing every pixel in the entire image. This approach reduces total computational cost while preserving recognition accuracy through strategic sampling of informative regions.
Data Source
AI summary
The present invention discloses a system and method for image processing and recognizing a scene of an image. The system utilizes a Multi-mode scalable network system and regrouping pipeline. The system is AI based system which uses neuro network. The system includes a pre-processing, processing and a post-processing unit. The system uses optical information recorded from the camera of a mobile device to extract and analyze the content in an image such as a photo or video clip. Based on the retrieved information, a label is given to best describe the scene of the image.