In the paper, MONDRIAN is introduced as an edge system for efficient object detection on high-resolution video streams. The system utilizes Compressive Packed Inference to optimize processing costs and maximize parallelism. Through various techniques, MONDRIAN outperforms existing baselines in accuracy and throughput.
The content discusses the challenges faced in high-resolution video analytics and the limitations of current lightweight models and system optimization techniques. It proposes a novel approach to address these challenges effectively. By dynamically rescaling ROIs and intelligently packing them into canvases, MONDRIAN achieves significant improvements in accuracy and throughput.
Key points include the introduction of MONDRIAN as an edge system for object detection, the innovative Compressive Packed Inference approach, challenges in safe area estimation and ROI packing, and additional optimizations for improved performance.
The evaluation showcases the superior performance of MONDRIAN compared to baseline systems across different datasets, models, and devices. Through detailed analysis and experimentation, the effectiveness of the proposed system is demonstrated.
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by Changmin Jeo... at arxiv.org 03-13-2024
https://arxiv.org/pdf/2403.07598.pdfDeeper Inquiries