Difference-Guided Video Analysis for High-Resolution Object Detection
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Solution Overview
Problem
High-resolution video analysis is inefficient due to increased processing times and resource consumption, making it challenging to detect objects accurately and efficiently, especially in high-resolution videos like 8K resolution.
Innovation Solution
A difference-guided video analysis system (DGS) generates a difference feature map from lower-resolution frames to detect objects in higher-resolution frames, using a spatial attention layer to improve processing efficiency and accuracy by cropping and analyzing specific object areas within higher-resolution frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution video frames are processed directly for object detection, then detection accuracy is improved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent divides the high-resolution video processing into two stages: first processing downsampled low-resolution frames to generate difference feature maps, then using these maps to guide selective processing of high-resolution frames. This segmentation allows the system to benefit from both low-resolution speed and high-resolution accuracy without paying the full computational cost of processing entire high-resolution frames.
Solution Approach 2:
The patent performs preliminary processing on low-resolution frames to generate difference feature maps before processing high-resolution frames. These difference feature maps contain preliminary object location information that guides subsequent high-resolution frame processing, reducing the search space and computational requirements for accurate detection.
2Measurement precision
If high-resolution video frames are processed directly for object detection, then detection accuracy is improved, but resource consumption increases significantly
Solution Approach 1:
The patent segments the computational workload by processing low-resolution frames first to extract difference feature maps, then using these maps to guide processing of only relevant regions in high-resolution frames. This reduces overall computational resource consumption while maintaining detection accuracy.
Solution Approach 2:
The patent introduces difference feature maps as an intermediary between low-resolution and high-resolution processing. These maps serve as a bridge that transfers object location information from low-resolution frames to guide high-resolution frame processing, reducing the computational burden of direct high-resolution analysis.
3Productivity
If lower-resolution frames are used for object detection, then processing efficiency is improved, but detection accuracy deteriorates
Solution Approach 1:
The patent segments the processing into two levels: low-resolution frames processed efficiently to generate difference feature maps, and high-resolution frames processed selectively using these maps as guidance. This allows the system to achieve both processing efficiency from low-resolution frames and detection accuracy from high-resolution frames.
Solution Approach 2:
The patent performs preliminary processing on low-resolution frames to generate difference feature maps that contain object location information. These maps are then used to guide subsequent high-resolution frame processing, allowing the system to leverage the speed of low-resolution processing while achieving high-resolution detection accuracy.
Data Source
AI summary
A method can include obtaining, from a video having a first resolution, a set of frames having a second resolution. The first resolution can be higher than the second resolution. The set of frames can include a first frame and a second frame. The method can include generating a difference feature map. The method can include obtaining a third frame having the first resolution. The method can include detecting, based on the difference feature map, a first location of a first object in the third frame. The method can include cropping, from the third frame, a first cropped area. The first cropped area can be smaller than a third frame area. The method can include generating, based on a feature map and the difference feature map, a spatial attention layer. The method can include detecting, by the spatial attention layer, the first object in the first cropped area.