Image Segmentation via Motion-Aware Feature Maps
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Solution Overview
Problem
Deep learning-based image segmentation techniques face challenges in achieving high accuracy for images with significant object motion, resulting in inconsistent and robust object detection due to variations in segmentation accuracy from frame to frame.
Innovation Solution
An image segmentation method that extracts motion information from frames and reflects it into class-specific feature maps generated by a deep learning model, improving segmentation accuracy without additional training, and reduces computational costs by reusing segmentation results for frames with minimal motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models trained on still images are directly applied to perform image segmentation on a frame-by-frame basis, then the approach is simple and computationally efficient, but it does not achieve high segmentation accuracy for images with significant object motion
Solution Approach 1:
The patent introduces motion information as an intermediary element that mediates between the input image frames and the deep learning model. By extracting motion information from video frames and integrating it into the feature maps, the system enhances the model's ability to handle object motion without requiring complex model retraining. This intermediary motion information bridges the gap between simple frame-by-frame processing and accurate motion-aware segmentation.
Solution Approach 2:
The patent changes the parameters fed into the deep learning model by incorporating motion information alongside visual features. Instead of only using standard image features from each frame, the system modifies the input parameters to include motion vectors or optical flow data, which helps the model better understand and segment moving objects. This parameter enhancement improves segmentation accuracy without fundamentally changing the model architecture.
2Measurement precision
If motion information is extracted and reflected into class-specific feature maps to improve segmentation accuracy for moving objects, then segmentation accuracy improves, but computational cost increases
Solution Approach 1:
The patent performs preliminary action by extracting motion information in advance before feeding it into the deep learning model. By pre-computing motion vectors or optical flow from consecutive video frames and preparing motion-enhanced feature maps beforehand, the system reduces the computational burden during the actual segmentation process. This preliminary preparation of motion data allows the model to focus on classification rather than motion detection, thereby reducing overall computational cost.
3Reliability
If deep learning models are retrained with motion information to achieve consistent segmentation accuracy, then segmentation robustness improves, but training time and computational resources increase
Solution Approach 1:
The patent enables the system to self-service by automatically extracting and integrating motion information without requiring manual retraining of the deep learning model. The existing model processes both visual features and motion information in conjunction, allowing the system to adapt to motion scenarios autonomously. This self-service approach maintains segmentation robustness while avoiding the time-consuming process of collecting motion-labeled data and retraining models.
Data Source
AI summary
Provided are a method for image segmentation and a system therefor. The method according to some embodiments may include acquiring a deep learning model trained through an image segmentation task, extracting motion information associated with a current frame of a given image, and performing image segmentation for the current frame by reflecting the extracted motion information into class-specific feature maps of the deep learning model, the class-specific feature maps being generated by the deep learning model based on the current frame.