Object Detection Across Disparate Fields of View
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems face challenges in recognizing and tracking objects across disparate fields of view, particularly in identifying correlations and characteristics of objects in multiple images captured by different recording devices with non-overlapping or partially overlapping fields of view.
Innovation Solution
A data processing system equipped with an object detection component, an object classification component, and an object matching component uses locality sensitive hashing and an inverted index central data structure to detect, classify, and correlate objects across different images, generating descriptors and probability identifiers to determine object correlations and characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional object matching methods are used across disparate fields of view, then object correlation can be achieved, but processing efficiency is low due to high data dimensionality
Solution Approach 1:
The patent extracts only the essential and discriminative features from objects (such as color histograms, texture features, shape descriptors) rather than processing complete high-dimensional images. This extraction process reduces data dimensionality while preserving the key characteristics needed for object matching across different fields of view, thereby improving processing efficiency without sacrificing matching accuracy.
2Measurement precision
If complete object descriptors are generated for accurate matching, then object correlation accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements a two-stage matching process: first using lightweight, partial features (such as color histograms or simple shape descriptors) for rapid pre-screening of candidate objects, then applying more comprehensive and accurate descriptors only to the reduced set of candidates. This partial action approach achieves accurate object correlation while significantly reducing overall processing time compared to using complete descriptors for all objects.
3Measurement precision
If multiple classification categories are determined for objects, then object recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the object classification process into multiple hierarchical levels or independent classification tasks (such as object category, color, shape, size). Each segmentation handles a specific aspect of object description, allowing the system to determine multiple classification categories through modular, manageable components rather than a single complex system, thus improving recognition accuracy while controlling system complexity.
Data Source
AI summary
Object detection and across disparate fields of view are provided. A first image generated by a first recording device with a first field of view, and a second image generated by a second recording device with a second field of view can be obtained. An object detection component can detect a first object within the first field of view, and a second object within the second field of view. An object classification component can determine first and second level classification categories of the first object. Object components can correlate the first object with the second object based on the descriptor of the first object or a descriptor of the second object, and can determine a characteristic or the first object or the second object based on the correlation.