Attention Map Target Position Prediction Across Angles
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Solution Overview
Problem
Current computer vision technologies face challenges in accurately predicting the position of target objects across multiple images captured from different angles, as they require projecting objects into a common vector space and calibrating projection matrices, which can lead to errors due to changes in image capture apparatus pose.
Innovation Solution
An image processing method that acquires a first image and its attention map, along with a second image and its mask image, to predict the position of the target object in the second image without the need for projecting objects into a common vector space, using feature extraction and association through neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If objects are projected into a common vector space and projection matrices are calibrated, then target object position prediction across multiple images can be achieved, but errors occur due to changes in image capture apparatus pose
Solution Approach 1:
The patent introduces an attention map as an intermediary element that guides the neural network to focus on relevant regions in the first image when predicting target object positions in the second image. This attention mechanism serves as a mediator between the two images, allowing the system to transfer positional information without requiring rigid projection matrices that are sensitive to pose changes. The attention map dynamically adapts to different poses by highlighting important regions, thus resolving the contradiction between achieving accurate position prediction and maintaining reliability under pose variations.
2Measurement precision
If projection matrices are calibrated for multiple images, then target position prediction is possible, but the system complexity increases
Solution Approach 1:
The patent replaces the mechanical/mathematical system of projection matrices and coordinate transformations with a data-driven neural network approach. Instead of using rigid geometric projections that require complex calibration, the system uses a neural network with attention mechanisms to learn and predict target positions directly from image features. This substitution eliminates the need for projection matrix calibration while maintaining position prediction capability, thus reducing system complexity.
3Measurement precision
If attention maps are used to guide feature extraction, then target position prediction accuracy improves, but computational requirements increase
Solution Approach 1:
The attention map mechanism applies local quality by directing computational resources only to relevant regions of the image rather than processing the entire image uniformly. The attention map highlights specific areas containing the target object, allowing the neural network to focus its computational effort on these localized regions. This selective processing maintains high position prediction accuracy while reducing overall computational energy consumption compared to full-image processing.
Data Source
AI summary
The present disclosure relates to an image processing method and apparatus, an electronic device, and a storage medium. The method includes: acquiring a first image and a first attention map of a first target object in the first image, where the first attention map indicates the position of the first target object in the first image; acquiring a second image and a second mask image of the second image, where pixel values of pixels in the second mask image are equal; and obtaining, according to first input information and second input information, a first prediction result that indicates the position of the first target object in the second image, where the first input information is determined according to the first image and the first attention map, and the second input information is determined according to the second image and the second mask image.