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<title>Arena: A Patch-of-Interest ViT Inference Acceleration System for Edge-Assisted Video Analytics</title>
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<h1 class="title is-1 publication-title">Arena: A Patch-of-Interest ViT Inference Acceleration System for Edge-Assisted Video Analytics</h1>
<!-- <h1 class="subtitle is-1 publication-title"><span class="gray-text">CVPR 2024</span> <span class="red-text">(Highlight)</span></h1> -->
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://livioni.github.io/">Haosong Peng</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://couteaux123.github.io/">Wei Feng</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://lifuguan.github.io/">Hao Li</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://ray-zhan.github.io/">Yufeng Zhan</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://github.com/kimihe">Qihua Zhou</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=HtedN3oAAAAJ&hl=zh-CN">Yuanqing Xia</a><sup>1</sup>,
</span>
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<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Beijing Institute of Technology</span>
<span class="author-block"><sup>2</sup>Northwestern Polytechnical University</span>
<span class="author-block"><sup>3</sup>The Hong Kong Polytechnic University</span>
<!-- <span class="author-block"><sup>4</sup>Nanyang Technological Universityh</span>
<span class="author-block"><sup>5</sup>Baidu, Inc.</span>
<span class="author-block"><sup>6</sup>Institute of Artificial Intelligence, Hefei Comprehensive National Science Center</span> -->
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<h2 class="subtitle has-text-centered">
<span class="dnerf">Arena</span>: our patch-of-interest ViT inference acceleration system for edge-assisted video analytics. Due to the limited computing power of the camera, the extracted patches-of-interest are offloaded to an edge server for processing with its more powerful GPUs. MTPs stands for Memory Token Pools.</span>
</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
The advent of edge computing has made real-time intelligent video analytics feasible. Previous works, based on traditional model architecture (e.g., CNN, RNN, etc.), employ various strategies to filter out non-region-of-interest content to minimize bandwidth and computation consumption but show inferior performance in adverse environments. Recently, visual foundation models based on transformers have shown great performance in adverse environments due to their amazing generalization capability. However, they require a large amount of computation power, which limits their applications in real-time intelligent video analytics. In this paper, we find visual foundation models like Vision Transformer (ViT) also have a dedicated acceleration mechanism for video analytics. To this end, we introduce Arena, an end-to-end edge-assisted video inference acceleration system based on ViT. We leverage the capability of ViT that can be accelerated through token pruning by only offloading and feeding Patches-of-Interest (PoIs) to the downstream models. Additionally, we employ probability-based patch sampling, which provides a simple but efficient mechanism for determining PoIs where the probable locations of objects are in subsequent frames. Through extensive evaluations on public datasets, our findings reveal that Arena can boost inference speeds by up to 1.58\times1.58\times and 1.82\times1.82\times on average while consuming only 54% and 34% of the bandwidth, respectively, all with high inference accuracy.
</p>
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<h2 class="title is-3">Method</h2>
<img id="framework" src="./static/images/overview.png">
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<strong>Overview of proposed Arena.</strong> Given \(K\) continuous frames \(\{\hat{\mathbf{x}}^1, \mathbf{x}^2, \ldots, \mathbf{x}^K\}\) in an interval, Arena periodically operates in two distinct phases: keyframe inference (Left) for the first frame \(\hat{\mathbf{x}}^1\) and non-keyframe inference (Right) for the rest of the frames. Both two phases use the same network architecture with shared weights. Notably, we split the frame into nine patches only for demonstration..
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<h2 class="title is-3">Accuracy</h2>
<img id="framework" src="./static/images/acc.png">
<p>
The <strong>accuracy</strong> of different methods on two datasets. Arena can maintain accuracy losses within <strong>1%</strong> and <strong>4%</strong>.
</p>
</div>
</div>
<!--/ Visual Effects. -->
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<h2 class="title is-3">Bindwidth Usage</h2>
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<p>
The normalized <strong>bandwidth usage</strong> of different methods on two datasets.
</p>
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<h2 class="title is-3">End-to-end Latency</h2>
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<img id="framework" src="./static/images/latency.png">
<p>
The average <strong>end-to-end latency</strong> per frame of different methods on two datasets. End-to-end latency includes a breakdown of <strong>preprocessing, transmission</strong>, and <strong>inference time</strong>.
</p>
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<h2 class="title is-3">Visualization</h2>
<img id="framework" src="./static/images/example.png">
</img>
<p>
Visualization of Arena on two videos. In these two scenes, with a frame interval of 5, $m$ is set to 1 and 3 for MOT17 and AIC22, respectively, $p=0.9$, and $F=200$. Only the red patches in non-keyframe are used for transmission and inference.
<p>
</div>
</div>
<!--/ Method. -->
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<img id="framework" src="./static/images/Heatmap.png">
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<strong>Heatmaps</strong> of patches identified as PoIs, where darker areas indicate a higher frequency of offloading to the edge server.
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<div class="item item-steve">
<video poster="" id="steve" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/arena/MOT17-02.mp4"
type="video/mp4">
</video>
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<source src="./static/videos/arena/MOT17-04.mp4"
type="video/mp4">
</video>
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<source src="./static/videos/arena/MOT17-09.mp4"
type="video/mp4">
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<source src="./static/videos/arena/AIC22-c006.mp4"
type="video/mp4">
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<div class="item item-steve">
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<source src="./static/videos/arena/AIC22-c007.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-steve">
<video poster="" id="steve" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/arena/AIC22-c008.mp4"
type="video/mp4">
</video>
</div>
</div>
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</section>
<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@misc{peng2024arena,
title={Arena: A Patch-of-Interest ViT Inference Acceleration System for Edge-Assisted Video Analytics},
author={Haosong Peng and Wei Feng and Hao Li and Yufeng Zhan and Qihua Zhou and Yuanqing Xia},
year={2024},
eprint={2404.09245},
archivePrefix={arXiv},
primaryClass={cs.MM}
}</code></pre>
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