Video Panorama Segmentation Neural Network
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Solution Overview
Problem
Current video panorama segmentation technologies face challenges in accuracy and complexity due to the need for additional tracking modules to determine correspondence between video frames, leading to low representation of panoramic objects in single frame images.
Innovation Solution
A processor-implemented method that uses a neural network to iteratively process video features, generating a target object representation and subsequently a panorama segmentation result without requiring an object tracking module, simplifying the algorithm structure while improving segmentation accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an additional tracking module is required to obtain correspondence information between each video frame, then object tracking accuracy is improved, but network structure becomes complicated
Solution Approach 1:
The patent merges the tracking function with the segmentation function into a unified neural network architecture. The segmentation model directly processes video frames and outputs panoptic segmentation results with temporal consistency, eliminating the need for separate tracking modules while maintaining object correspondence accuracy across frames.
Solution Approach 2:
The neural network is designed to perform multiple functions simultaneously: it conducts panoptic segmentation, maintains temporal consistency across frames, and identifies object correspondence all within a single unified architecture. This multi-functional design removes the need for additional specialized tracking modules.
2Productivity
If panoptic segmentation is performed for each image without tracking, then processing speed is improved, but segmentation accuracy decreases
Solution Approach 1:
The patent implements continuous processing of video frames through a unified neural network that maintains temporal consistency. The model continuously refines segmentation results across frames, ensuring that object representations remain consistent over time while processing all frames through a single efficient architecture rather than separate discrete operations.
Data Source
AI summary
A processor-implemented method includes: obtaining a video feature of a video comprising a plurality of video frames; determining a target object representation of the video based on the video feature using a neural network; and generating a panorama segmentation result of the video based on the target object representation.