AI Object Location Prediction via Gaussian Mixture Models
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Solution Overview
Problem
Conventional object recognition systems are limited in predicting the near future location of dynamic objects, especially when considering sudden changes and interactions with static and dynamic backgrounds, as they primarily rely on past information and fail to account for objects not present in the current image frame.
Innovation Solution
A system utilizing artificial intelligence to predict the near future location of dynamic objects by learning from current and historical image data, employing a segmentation neural network, object post-processing, and object bounding residual estimation neural networks to generate Gaussian mixture models for visualizing potential object locations, both present and absent from the current frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional dynamic approach method is used to predict future location based on past and current location information, then the prediction process is simple and fast, but the prediction accuracy is insufficient because it cannot account for sudden changes in object behavior or interactions with the environment
Solution Approach 1:
The system segments the prediction task into multiple components: a segmentation neural network that processes the current image to identify static and dynamic objects, an object post-processing module that generates a static segmentation image by removing dynamic objects, and a residual estimation neural network that predicts location changes. This segmentation allows each component to specialize in specific aspects of the prediction problem, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The system performs preliminary processing by generating a static segmentation image that removes dynamic objects from the current image before prediction. This preliminary action creates a clean background representation that helps the residual estimation network focus on predicting only the location changes of dynamic objects, leading to more accurate predictions of sudden behavior changes.
2Reliability
If only current timing image data is used for object recognition and prediction, then the processing speed is fast and the system is simple, but the ability to predict objects not present in the current frame or sudden location changes is limited
Solution Approach 1:
The system performs preliminary processing by generating a static segmentation image that represents the background environment before the dynamic objects appear or move. This static segmentation image serves as a reference that helps predict the locations of objects that may not be visible in the current frame or may suddenly appear, improving prediction reliability without requiring extensive historical data processing.
3Measurement precision
If a single prediction location is provided by the conventional dynamic approach, then the output is simple and easy to process, but it fails to capture multiple possible future locations and sudden behavior changes of dynamic objects
Solution Approach 1:
The system uses the static segmentation image as feedback to guide the residual estimation neural network. The static segmentation provides background context that feeds back into the prediction process, enabling the network to infer multiple possible future locations by comparing the static background with the current dynamic scene. This feedback mechanism allows the system to generate multiple prediction outputs that capture uncertain future states.
Data Source
AI summary
The present disclosure relates to a system for predicting a near future location of an object, which predicts a near future location of a dynamic object, through learning for sampling a sample obtained by estimating a location of the dynamic object by learning an image at current timing, which is captured by a camera, based on artificial intelligence and for changing the sample of the dynamic object whose location has been estimated into a Gaussian mixture model (GMM) by using history data.