Object Classification via Albedo Variance Analysis
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Solution Overview
Problem
Current methods for classifying objects in images, particularly for surveillance and security applications, face challenges in efficiently distinguishing between animate and inanimate objects, especially in fast-moving scenarios like aerial spaces, where existing technologies struggle to accurately label objects like drones and birds.
Innovation Solution
The proposed method involves performing variance analysis, specifically Albedo variance analysis, on temporally sequential images using pixel array image sensors and deep learning neural networks to differentiate between animate and inanimate objects, with the capability to automatically track and label objects across frames, and optionally trigger responses for inanimate objects like drones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection and tracking methods are used, then objects can be detected and tracked across frames, but the accuracy of distinguishing between animate and inanimate objects is insufficient
Solution Approach 1:
The patent segments the classification process into distinct analytical components: detecting objects across sequential images, tracking their motion patterns, performing variance analysis on appearance changes, and classifying based on combined features. This segmentation allows each component to be optimized independently while maintaining overall system manageability.
Solution Approach 2:
The patent introduces temporal dimension by analyzing objects across multiple sequential images rather than single frames. By examining motion patterns over time and performing variance analysis across frames, the system adds a temporal dimension to the classification process, significantly improving the ability to distinguish animate from inanimate objects.
2Measurement precision
If variance analysis is applied to improve classification accuracy, then distinction between animate and inanimate objects improves, but processing time increases
Solution Approach 1:
The patent performs preliminary object detection and tracking to identify candidate objects before applying the computationally intensive variance analysis. By pre-filtering objects that require detailed classification and preparing motion patterns in advance, the system reduces the overall processing time while maintaining high classification accuracy.
Solution Approach 2:
The system applies variance analysis selectively to objects that require classification rather than processing all detected objects uniformly. By focusing computational resources on partial cases where classification is needed, the system achieves high accuracy without excessive processing time for all objects.
3Productivity
If deep learning neural networks are used for real-time classification, then classification speed improves, but computational resources required increase
Solution Approach 1:
The patent segments the computational workload into multiple stages: initial object detection, motion pattern tracking, variance analysis, and final classification using deep learning. This segmentation allows simpler operations to be performed first, reducing the number of objects that require intensive neural network processing, thereby lowering overall computational resource consumption.
Solution Approach 2:
The system performs preliminary filtering and feature extraction before applying deep learning models. By pre-processing images to extract relevant features and identify candidate objects, the system reduces the input size and complexity for the neural network, enabling faster real-time classification with reduced computational resource requirements.
Data Source
AI summary
A method for classification of objects in images includes obtaining a plurality of temporally sequential images; detecting at least one object of interest in the images; matching at least one detected object of interest across the plurality of the images; applying variance analysis on the object between the temporally sequential images; and based on variance analysis output, assigning at least one label to the object of interest.