Content-Aware Video Object Detection With Dynamic Branch Switching
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Solution Overview
Problem
Existing video object detection systems on mobile devices face challenges in maintaining consistent latency and accuracy due to insufficient feature extraction, inaccurate prediction, and high reconfiguration overhead, particularly when adapting to dynamically changing content and resource availability.
Innovation Solution
A cost and content-aware reconfiguration system that includes a scheduler with a cost-benefit analyzer and a content-aware accuracy prediction model, using a multi-branch execution kernel to optimize feature selection and branch configuration based on light-weight and heavy-weight features, while considering computational overhead and latency constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multi-model or multi-branch execution kernels are used to adapt to changing video content and resource availability, then adaptability is improved, but device complexity increases and switching overhead occurs
Solution Approach 1:
The system dynamically selects and switches between different execution kernels based on real-time video content analysis and resource availability. The scheduler continuously monitors frame characteristics (e.g., motion intensity, object complexity) and adapts the execution kernel configuration accordingly, making the system flexible rather than static.
Solution Approach 2:
The execution kernel is divided into multiple specialized branches, each optimized for specific video content types or resource conditions. Instead of using a single general-purpose kernel, the system segments functionality into specialized sub-kernels that can be selectively activated based on current requirements.
2Measurement precision
If frequent switching between execution kernels is performed to optimize for varying video content, then accuracy is improved, but latency increases due to switching overhead
Solution Approach 1:
The system employs periodic action by using light-weight trackers for regular frame processing and only activating full object detection kernels periodically or when content changes significantly. This reduces the frequency of expensive kernel switches while maintaining detection accuracy through temporal aggregation of tracking data.
Solution Approach 2:
The system performs preliminary analysis of video content characteristics (such as motion intensity, scene complexity) before deciding whether to switch kernels. This preliminary assessment allows the scheduler to avoid unnecessary kernel switches and only transition when truly beneficial for accuracy.
3Productivity
If content-aware reconfiguration is implemented to optimize latency and accuracy, then detection performance is improved, but computational overhead increases
Solution Approach 1:
The system applies different levels of processing quality to different regions or aspects of video content. For example, regions with high motion or detected objects receive full detection processing, while static or empty regions use lighter tracking or are skipped entirely, optimizing the balance between performance and computational cost.
Data Source
AI summary
A system may receive video information. The system may select a combination of light-weight features and heavy weight features. The light-weight features may be extracted from the video information and the heavy weight features not extracted. The system may forecast, based on the light-weight features and the heavy weight features, accuracy, and latency metrics for performing the object detection and tracking using a plurality of candidate branch configurations, respectively. The system may select a branch configuration from the plurality of candidate branch configurations in response to satisfaction of an optimization criterion. The system may perform object detection and tracking based on the selected branch configuration.