Neural Network Object Recognition Using Location Histograms
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Solution Overview
Problem
Conventional image processing techniques for object recognition in media content are often inaccurate, inefficient, and require significant computational and data resources, especially when utilizing GPS coordinates for location-based object detection, leading to inconsistent results.
Innovation Solution
The development of a system that identifies regions corresponding to geographical areas, acquires training images associated with recognized objects and locations, and uses a neural network to predict object recognition outputs based on histogram metrics and location information, reducing the need for extensive data storage and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object detection and recognition approaches are used, then objects can be detected in images, but the accuracy and effectiveness are reduced
Solution Approach 1:
The patent introduces location information as an additional dimension to traditional image-based object recognition. By combining spatial coordinates with image data, the system creates a multi-dimensional recognition framework that improves accuracy and reliability beyond conventional two-dimensional image analysis alone
Solution Approach 2:
The patent transforms location information from simple GPS coordinates into histogram metrics that capture the distribution and frequency of objects at different locations. This parameter transformation enables more effective feature extraction and improves recognition accuracy by representing spatial information in a statistically meaningful way
2Adaptability or versatility
If GPS coordinates are used for location-based object detection, then location information can be incorporated, but computational resources and data storage requirements increase significantly
Solution Approach 1:
The patent extracts only the essential features from location data by converting GPS coordinates into histogram metrics that capture object distribution patterns. This extraction process removes redundant information while retaining the key spatial characteristics needed for recognition, reducing computational and storage requirements
Solution Approach 2:
Instead of using detailed GPS coordinates directly for recognition, the patent inverts the approach by aggregating location data into histogram metrics. This inversion transforms continuous spatial data into discrete, compressed representations that are more efficient for processing while maintaining recognition capability
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can identify a set of regions corresponding to a geographical area. A collection of training images can be acquired. Each training image in the collection can be associated with one or more respective recognized objects and with a respective region in the set of regions. Histogram metrics for a plurality of object categories within each region in the set of regions can be determined based at least in part on the collection of training images. A neural network can be developed based at least in part on the histogram metrics for the plurality of object categories within each region in the set of regions and on the collection of training images.