Image Prediction System Using Gaze Region Segmentation
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Solution Overview
Problem
Existing future image prediction technologies struggle to accurately identify objects in predicted images, particularly in autonomous driving applications, due to their inability to effectively predict changes in objects like vehicles and people, leading to safety concerns.
Innovation Solution
An image prediction system that includes a gaze unit, a working memory unit, and a generation model unit, which determines and processes gaze regions to generate prediction images, integrating information from both the gaze unit and working memory unit to enhance object recognition in predicted images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If future image prediction is performed for each pixel set divided by a predetermined grid, then prediction coverage is improved, but object identification accuracy deteriorates and prediction time increases
Solution Approach 1:
The patent segments the image into multiple gaze regions based on object detection, where each gaze region corresponds to a specific object of interest. This allows the system to process and predict changes in each object independently while maintaining focus on relevant areas, thereby improving object identification accuracy without sacrificing overall prediction coverage.
Solution Approach 2:
The patent applies different processing strategies to different gaze regions based on their content and importance. By identifying objects such as vehicles and pedestrians and creating gaze regions around them, the system can apply enhanced prediction methods to these critical areas while using standard methods for background regions, thus improving local object identification accuracy.
2Area of stationary object
If future image prediction is performed for each pixel set divided by a predetermined grid, then prediction coverage is improved, but prediction time increases
Solution Approach 1:
The patent extracts only the necessary gaze regions containing objects of interest from the entire image grid. Instead of processing every pixel set uniformly, the system identifies and focuses computation on regions where objects like vehicles and pedestrians are detected, thereby reducing overall prediction time while maintaining comprehensive coverage of important areas.
Solution Approach 2:
The patent applies partial action by processing only the gaze regions that contain objects of interest rather than the entire image grid. This selective processing approach reduces computational burden and prediction time while still providing comprehensive prediction coverage for the critical areas where objects are detected.
3Measurement precision
If gaze regions are processed to generate prediction images, then object recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional gaze region determination unit that performs object detection, gaze region identification, and prediction processing within a single integrated component. This universal approach handles multiple tasks (object detection, region segmentation, prediction) through one unit, thereby improving object recognition accuracy while avoiding the need for separate complex subsystems.
Solution Approach 2:
The patent merges the functions of object detection, gaze region identification, and prediction generation into an integrated processing pipeline. By combining these functions in a unified system architecture where the gaze region determination unit works closely with the prediction generation unit, the system achieves high object recognition accuracy without proportionally increasing overall device complexity.
Data Source
AI summary
To generate a prediction image in which an outline of an object is clear and existence of the object is easily recognized, the image prediction system that generates a future prediction image and includes a gaze unit, a working memory unit, a control unit, and a generation model unit. The gaze unit controls a region including an object included in an observation image as a first gaze region. The working memory unit controls the first gaze region as a second gaze region when a difference in the first gaze region between the observation image and a prediction image is equal to or less than a predetermined value. The generation model unit generates prediction images of the first gaze region and the second gaze region. The control unit integrates the prediction image of the first gaze region and the prediction image of the second gaze region to generate a prediction image.