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presentation.html
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<!DOCTYPE html><html lang="POSIX"><head><title>Traffic monitoring system</title><meta property="og:title" content="Traffic monitoring system"><meta name="author" content="Vladislav Serkov"><meta property="article:author" content="Vladislav Serkov"><meta charset="UTF-8"><meta name="viewport" content="width=device-width,height=device-height,initial-scale=1.0"><meta name="apple-mobile-web-app-capable" content="yes"><meta http-equiv="X-UA-Compatible" content="ie=edge"><meta property="og:type" content="website"><meta name="twitter:card" content="summary"><style>@media screen{body[data-bespoke-view=""] .bespoke-marp-parent>.bespoke-marp-osc>button,body[data-bespoke-view=next] .bespoke-marp-parent>.bespoke-marp-osc>button,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-info-container button,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-note-container 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/*!
* Marp default theme.
*
* @theme default
* @author Yuki Hattori
*
* @auto-scaling true
* @size 16:9 1280px 720px
* @size 4:3 960px 720px
*/div#\:\$p>svg>foreignObject>section,div#\:\$p>svg>foreignObject>section 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class="bespoke-marp-osc"><button data-bespoke-marp-osc="prev" tabindex="-1" title="Previous slide">Previous slide</button><span data-bespoke-marp-osc="page"></span><button data-bespoke-marp-osc="next" tabindex="-1" title="Next slide">Next slide</button><button data-bespoke-marp-osc="fullscreen" tabindex="-1" title="Toggle fullscreen (f)">Toggle fullscreen</button><button data-bespoke-marp-osc="presenter" tabindex="-1" title="Open presenter view (p)">Open presenter view</button></div><div id=":$p"><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section data-footer="somecompany challenge 2023 - Vladislav Serkov" data-theme="default" data-style=".columns {
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;--marpit-advanced-background-split:50%;" data-size="16:9" data-marpit-advanced-background="content" data-marpit-advanced-background-split="right">
<h1 id="traffic-monitoring-system-for-counting-incoming-and-outgoing-vehicles">Traffic monitoring system for counting incoming and outgoing vehicles</h1>
<footer>somecompany challenge 2023 - Vladislav Serkov</footer>
</section>
</foreignObject><foreignObject width="1280" height="720" data-marpit-advanced-background="pseudo"><section data-footer="somecompany challenge 2023 - Vladislav Serkov" data-theme="default" data-style=".columns {
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display: grid;
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gap: 1rem;
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" lang="POSIX" data-marpit-pagination="2" style="--footer:somecompany challenge 2023 - Vladislav Serkov;--paginate:true;--theme:default;--style:.columns {
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;" data-marpit-pagination-total="10" data-size="16:9">
<h2 id="implementation-demonstration">Implementation demonstration</h2>
<p align="center">
<video src="../outputs/cyberpunk.mp4" controls width="80%">
</p>
<footer>somecompany challenge 2023 - Vladislav Serkov</footer>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="3" data-footer="somecompany challenge 2023 - Vladislav Serkov" data-paginate="true" data-theme="default" data-style=".columns {
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gap: 1rem;
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justify-content: start;
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;" data-marpit-pagination-total="10" data-size="16:9">
<h2 id="system-overview">System overview</h2>
<div class="mermaid">
graph LR
subgraph Legend
direction TB
imp[Implemented]
nimp{{Not implemented}}
end
input[Video feed]
subgraph Offline
direction LR
roadplane[Road plane detection]
lane{{Lane detection}}
calib{{Sensor calibration}}
end
subgraph Online
direction LR
detect[Vehicle detection]
track[Vehicle tracking]
count[Vehicle counting]
dir[Direction estimation]
speed{{Speed estimation}}
end
output[(Tracks DB)]
input --> Offline
input --> detect
roadplane --> detect
lane --> count
lane --> dir
detect --> track
track --> dir
track --> count
track --> speed
calib --> speed
dir --> output
count --> output
speed --> output
</div>
<footer>somecompany challenge 2023 - Vladislav Serkov</footer>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="4" data-footer="somecompany challenge 2023 - Vladislav Serkov" data-paginate="true" data-theme="default" data-style=".columns {
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" lang="POSIX" data-marpit-pagination="4" style="--footer:somecompany challenge 2023 - Vladislav Serkov;--paginate:true;--theme:default;--style:.columns {
display: grid;
grid-template-columns: repeat(2, minmax(0, 1fr));
gap: 1rem;
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section {
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;" data-marpit-pagination-total="10" data-size="16:9">
<h2 id="road-plane-segmentation">Road plane segmentation</h2>
<p>Well-researched topic with many both real-time and slower-than-real-time approaches.</p>
<div class="columns">
<div>
<h6 id="panoptic-segmentation-model">Panoptic segmentation model</h6>
<p><video src="../outputs/cyberpunk.mp4" width="100%"></video><br />
I'm using a <em>SegFormer</em> model from <img class="emoji" draggable="false" alt="🤗" src="https://cdn.jsdelivr.net/gh/jdecked/[email protected]/assets/svg/1f917.svg" data-marp-twemoji=""/></p>
</div>
<div>
<h6 id="dataset">Dataset</h6>
<p><img src="https://github.com/bdd100k/bdd100k/blob/master/doc/images/teaser.gif?raw=true" alt="bdd100k" /><br />
trained on <em>Cityscapes</em> dataset.</p>
</div>
<div>
<footer>somecompany challenge 2023 - Vladislav Serkov</footer>
</section>
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;" data-marpit-pagination-total="10" data-size="16:9">
<h2 id="road-plane-segmentation-1">Road plane segmentation</h2>
<h3 id="%F0%9F%92%A3%F0%9F%A4%AF%F0%9F%98%AD-licences-%F0%9F%98%AD%F0%9F%A4%AF%F0%9F%92%A3"><img class="emoji" draggable="false" alt="💣" src="https://cdn.jsdelivr.net/gh/jdecked/[email protected]/assets/svg/1f4a3.svg" data-marp-twemoji=""/><img class="emoji" draggable="false" alt="🤯" src="https://cdn.jsdelivr.net/gh/jdecked/[email protected]/assets/svg/1f92f.svg" data-marp-twemoji=""/><img class="emoji" draggable="false" alt="😭" src="https://cdn.jsdelivr.net/gh/jdecked/[email protected]/assets/svg/1f62d.svg" data-marp-twemoji=""/> Licences <img class="emoji" draggable="false" alt="😭" src="https://cdn.jsdelivr.net/gh/jdecked/[email protected]/assets/svg/1f62d.svg" data-marp-twemoji=""/><img class="emoji" draggable="false" alt="🤯" src="https://cdn.jsdelivr.net/gh/jdecked/[email protected]/assets/svg/1f92f.svg" data-marp-twemoji=""/><img class="emoji" draggable="false" alt="💣" src="https://cdn.jsdelivr.net/gh/jdecked/[email protected]/assets/svg/1f4a3.svg" data-marp-twemoji=""/></h3>
<p><strong>SegFormer</strong> (<a href="https://github.com/NVlabs/SegFormer/blob/master/LICENSE">https://github.com/NVlabs/SegFormer/blob/master/LICENSE</a>)</p>
<blockquote>
<p>3.3 Use Limitation. The Work and any derivative works thereof only may be used or intended for use non-commercially. Blah blah ...</p>
</blockquote>
<p><strong>Cityscapes</strong> (<a href="https://www.cityscapes-dataset.com/license/">https://www.cityscapes-dataset.com/license/</a>)</p>
<blockquote>
<p>Blah blah ... you may not use the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain. Blah blah goes on...</p>
</blockquote>
<p><strong>Bottom line</strong>: Need to be careful with the choice of the model and dataset.</p>
<footer>somecompany challenge 2023 - Vladislav Serkov</footer>
</section>
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;" data-marpit-pagination-total="10" data-size="16:9">
<h2 id="interlude-lane-detection">Interlude: lane detection</h2>
<div class="columns">
<div>
<ul>
<li>Could (and should) be the same segmentation model. <em>BDD100K</em> dataset includes lane annotations.</li>
<li>Alternatively, lanes can be inferred from vehicle trajectories themselves. <strong>Gotchas</strong>:
<ul>
<li>Large memory footprint: stores all trajectories in memory.</li>
<li>Not real-time: uses clustering.</li>
<li>Unclear assignment in case of lane changes.</li>
</ul>
</li>
</ul>
</div>
<div>
<p><img src="../outputs/clustered_traj.jpg" alt="Clustered trajectories" /><br />
<em>Almost all vehicle trajectories correctly clustered into 4 groups by lanes they use</em></p>
</div>
</div>
<footer>somecompany challenge 2023 - Vladislav Serkov</footer>
</section>
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<h2 id="vehicle-detection-and-tracking">Vehicle detection and tracking</h2>
<ul>
<li>Basically a solved problem.</li>
<li>Plethora of freely available models and datasets.</li>
<li>Porting to different hardware is easy.<br />
<br /></li>
</ul>
<p>The implementation uses <strong>YOLOv8n</strong> model from Ultralytics trained on <strong>COCO</strong> dataset which includes cars, trucks, buses, etc.<br />
MOT is performed using <strong>ByteTrack</strong>: good balance between speed and accuracy.</p>
<footer>somecompany challenge 2023 - Vladislav Serkov</footer>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="8" data-footer="somecompany challenge 2023 - Vladislav Serkov" data-paginate="true" data-theme="default" data-style=".columns {
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<h2 id="counting-direction-and-speed-estimation">Counting, direction and speed estimation</h2>
<ul>
<li><strong>Counting</strong> is supported by the tracking algorithm.
<ul>
<li>The algorithm assigns a unique ID to each detection.</li>
<li>Discarding spurious detections by enforcing minimum length of the trajectory (in number of frames).<br />
<br /></li>
</ul>
</li>
<li><strong>Direction</strong> is estimated by comparing the first and the last position of the trajectory.
<ul>
<li>Assumes that the directions can be differentiated by either the X or Y axis.</li>
<li>More robust approach uses the info about the lane the vehicle is in.<br />
<br /></li>
</ul>
</li>
<li>For <strong>speed estimation</strong> depth estimation is the king.</li>
</ul>
<footer>somecompany challenge 2023 - Vladislav Serkov</footer>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="9" data-footer="somecompany challenge 2023 - Vladislav Serkov" data-paginate="true" data-theme="default" data-style=".columns {
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<h2 id="counting-direction-and-speed-estimation-1">Counting, direction and speed estimation</h2>
<div class="columns">
<div>
<h6 id="speed-estimation">Speed estimation</h6>
<ul>
<li>Requires additional <strong>knowledge</strong> about the environment:
<ul>
<li>Camera in-/extrinsics</li>
<li>Scene depth</li>
<li>Reference object size for scale (optional)</li>
</ul>
</li>
<li>Or additional <strong>hardware</strong>:
<ul>
<li>Stereo camera</li>
<li>Radar/laser sensor</li>
</ul>
</li>
</ul>
</div>
<div>
<h6 id="monocular-depth-estimation">Monocular depth estimation</h6>
<p><img src="https://github.com/TRI-ML/vidar/blob/main/media/figs/packnet.gif?raw=true" alt="Monocular depth estimation" /><br />
<em>3D Packing for Self-Supervised Monocular Depth Estimation (CVPR 2020, oral)</em></p>
</div>
</div>
<footer>somecompany challenge 2023 - Vladislav Serkov</footer>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="10" data-footer="somecompany challenge 2023 - Vladislav Serkov" data-paginate="true" data-theme="default" data-style=".columns {
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" lang="POSIX" data-marpit-pagination="10" style="--footer:somecompany challenge 2023 - Vladislav Serkov;--paginate:true;--theme:default;--style:.columns {
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<h2 id="counting-direction-and-speed-estimation-2">Counting, direction and speed estimation</h2>
<p>Recent advances in monocular depth estimation: <strong>Towards Zero-Shot Scale-Aware Monocular Depth Estimation (ICCV 2023)</strong> by Toyota Research Institute (<a href="https://arxiv.org/abs/2306.17253">https://arxiv.org/abs/2306.17253</a>)</p>
<blockquote>
<p>In this work we introduce ZeroDepth, a novel monocular depth estimation framework capable of predicting metric scale for arbitrary test images from different domains and camera parameters. ... achieved a new state-of-the-art in both settings using the same pre-trained model, outperforming methods that train on in-domain data and require test-time scaling to produce metric estimates</p>
</blockquote>
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