AI Product Placement Detection via Pattern Elimination
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Solution Overview
Problem
Current computer vision systems for logo detection and product placement measurement are inefficient due to high false positives, require pre-training with extensive image catalogs, and struggle to accurately identify and value sponsored assets in complex media environments, especially with the introduction of virtual objects and dynamic sponsorship deals.
Innovation Solution
A system that inverts the training method for computer vision models by detecting placement patterns and eliminating unnecessary image fields, using machine learning and artificial intelligence to recognize and eliminate additional data, thereby reducing the visual search area and improving efficiency, and incorporates virtual object records for enhanced brand visibility and real-time valuation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computer vision models are trained using traditional methods with extensive image catalogs, then the model can recognize logos and patterns, but the system becomes inefficient, requires large computational resources, and produces high false positives
Solution Approach 1:
The patent inverts the traditional computer vision training approach by teaching the model to detect placement patterns and eliminate unnecessary image fields rather than training to recognize specific logos from extensive catalogs. This inversion fundamentally changes how the system processes visual data, reducing false positives and improving efficiency while maintaining detection accuracy
Solution Approach 2:
The system extracts and eliminates unnecessary image fields and additional data from the visual search space. By removing irrelevant information through the elimination process, the model focuses computational resources only on relevant placement patterns, significantly reducing processing time and resource consumption while maintaining high accuracy
2Measurement precision
If the computer vision model processes all image fields to detect patterns, then comprehensive detection is achieved, but processor and memory consumption increase significantly
Solution Approach 1:
The model extracts and eliminates unnecessary image fields from processing, retaining only the essential placement patterns needed for accurate detection. This extraction process significantly reduces the computational workload on processors and memory requirements while maintaining comprehensive detection capability for relevant sponsored assets
Solution Approach 2:
Instead of processing all image fields to ensure comprehensive detection, the inverted approach starts by eliminating unnecessary fields and only processes what remains. This reversal of the traditional processing sequence achieves the same detection thoroughness with dramatically reduced computational energy consumption
3Measurement precision
If traditional logo detection methods are used, then the system can identify sponsored assets, but it cannot accurately value them in real-time or handle virtual objects
Solution Approach 1:
The system incorporates dynamic elements by enabling real-time valuation of sponsored assets as they appear in media content. The model can process and value sponsored assets in real-time rather than requiring pre-processing or static analysis, adapting to dynamic media environments and providing immediate valuation data for virtual and physical assets alike
Solution Approach 2:
The unified model performs multiple functions including identification, measurement, and valuation of sponsored assets in a single processing pipeline. This multi-functional approach handles both virtual and physical assets, traditional and dynamic sponsorship deals, eliminating the need for separate systems for different valuation tasks
Data Source
AI summary
A system, method, and apparatus for identifying product placement utilizing a computer vision, artificial intelligence, and/or machine-learning model that includes an object recognition model. The computer vision, artificial intelligence, and/or machine-learning model can be trained to recognize each placement pattern in a first directory of placement patterns, eliminate additional data, where the additional data includes data not recognized as being in the first directory of placement patterns, and recognize each first pixel pattern in a second directory of pixel patterns. The system, method, and apparatus can be utilized to recognize, using the computer vision, artificial intelligence, and/or machine-learning model, the placement patterns present in an image; eliminate the additional data from the image, construct a modified image including the recognized placement patterns; and identify the first pixel patterns present in the modified image.