Adaptive Video Subsampling for Energy-Efficient Object Detection
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Solution Overview
Problem
Existing video subsampling methods for energy-efficient object detection in computer vision often compromise visual detail, as they are agnostic to semantic information in frames, leading to potential performance degradation in object detection tasks for mobile and autonomous platforms.
Innovation Solution
An adaptive video subsampling system that uses objectness and intensity-based segmentation to determine spatial sampling patterns, creating a binary mask for subsampling based on object presence and movement, allowing for energy savings while maintaining object detection performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If video subsampling is applied to save energy, then energy efficiency is improved, but visual detail and object detection performance deteriorate
Solution Approach 1:
The patent applies different sampling rates to different spatial regions of the video frame based on their semantic importance. Regions containing objects of interest are sampled at higher rates to preserve visual detail, while background regions are sampled at lower rates to save energy. This local differentiation resolves the contradiction by ensuring energy savings do not compromise object detection performance in critical areas.
Solution Approach 2:
The subsampling strategy is dynamically adjusted based on the detected object's position, size, and motion characteristics in each frame. The sampling pattern adapts in real-time to track moving objects and maintain detection performance while optimizing energy consumption as the scene changes.
2Use of energy by moving object
If uniform subsampling is applied across the entire frame, then energy savings are achieved, but object detection performance degrades due to loss of semantic information
Solution Approach 1:
Instead of uniform subsampling, the patent implements region-adaptive subsampling where the sampling rate varies across different spatial locations. High-priority regions containing objects are sampled more frequently than low-priority background regions, maintaining detection reliability while achieving energy savings through selective downsampling of less important areas.
Solution Approach 2:
The video frame is segmented into multiple regions with different sampling priorities based on object presence and semantic importance. This segmentation allows the system to apply different subsampling strategies to different parts of the frame, preserving object detection performance in critical regions while reducing energy consumption in non-critical regions.
3Measurement precision
If high-resolution video is processed in real-time, then object detection accuracy is maintained, but energy consumption increases significantly
Solution Approach 1:
The system processes high-resolution data only in regions where objects are detected or likely to be present, while using lower resolution processing for background regions. This local quality differentiation maintains object detection accuracy in critical areas while significantly reducing the overall computational energy requirements.
Solution Approach 2:
Instead of processing the entire high-resolution frame at full detail, the system applies full-resolution processing only to necessary regions (partial action), achieving sufficient detection accuracy without the excessive energy cost of processing every pixel at maximum resolution.
Data Source
AI summary
Various embodiments of systems and methods for adaptive video subsampling for energy-efficient object detection are disclosed herein.