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<head>
<meta http-equiv="Content-Language" content="zh-cn">
<meta http-equiv="Content-Type" content="text/html; charset=gb2312">
<title>DeepPS</title>
<style>
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<meta http-equiv="Content-Language" content="zh-cn">
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<title>DeepPS</title>
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<html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns="http://www.w3.org/TR/REC-html40">
<body>
<table class="MsoNormalTable" border="0" cellpadding="0" width="1217" id="table3" height="35">
<tr>
<td valign="top" style="width: 1211px; height: 31px; padding: .75pt" align="left">
<p class="text">
<span lang="en-us"><font face="Calibri" size="5" color="#0000FF">
<b>Vision-based Parking-slot Detection: A DCNN-based Approach and A
Large-scale Benchmark Dataset</b></font></span><p class="text">
<span lang="en-us"><font face="Calibri" size="4" color="#0000FF">
Lin Zhang<sup>1</sup>, Junhao Huang</font></span><font face="Calibri" size="4" color="#0000FF"><sup>1</sup><span lang="en-us">,
Xiyuan Li</span><sup>1</sup><span lang="en-us">, and Lu Xiong</span><sup>2</sup></font><p class="text">
<span lang="en-us"><font face="Calibri" size="4" color="#0000FF"><sup>1</sup>School
of Software Engineering, Tongji University, Shanghai, China</font></span><p class="text">
<font face="Calibri" size="4" color="#0000FF"><sup>2</sup><span lang="en-us">Intelligent
Vehicle Institute, Tongji University,
Shanghai, China</span></font></td>
</tr>
</table>
<hr>
<p><span lang="en-us"><b><font face="Calibri" size="5">Introduction</font></b></span></p>
<p>
<span style="font-size: 13pt; font-family: Calibri; color: windowtext" lang="EN-US">
This is the website for our paper "<a href="parkingslot.pdf">Vision-based Parking-slot Detection: A DCNN-based Approach and A Large-scale Benchmark Dataset<span style="color: #000000">,
IEEE Trans. Image Processing 27 (11) 5350-5364, 2018</span></a>"</span></p>
<hr>
<p><span lang="en-us"><b><font face="Calibri" size="5">Tongji Parking-slot Dataset
2.0</font></b></span></p>
<p>
<span lang="en-us"><font face="Calibri" style="font-size: 13pt">
<a href="https://drive.google.com/open?id=1i1pSnkIRyTgt6kmWr_sN6jrasX2HngWc">ps2.0.zip</a></font></span></p>
<p>
<span lang="en-us"><font face="Calibri" style="font-size: 13pt">The images in
this dataset were surround-view images synthesized from four low-cost fisheye
cameras. Various parking-slot types were considered, including the vertical
ones, the parallel ones, and the slant ones. When collecting outdoor samples,
different illumination conditions and weather conditions were considered.
Typical image samples contained in this dataset is shown below. </font></span></p>
<p>
<img border="0" src="ps%20image%20samples.jpg" width="914" height="435"></p>
<p>
<span lang="en-us"><font face="Calibri" style="font-size: 13pt">This dataset
contains 9827 training images and 2338 test images. In order to test the
performance of a parking-slot detection algorithm under different special
conditions, test images are also grouped into 6 categories,</font></span></p>
<table border="1" width="31%">
<tr>
<td width="232">
<p align="center"><b><font face="Calibri">Subset</font></b></td>
<td>
<p align="center"><b><font face="Calibri">Number of samples</font></b></td>
</tr>
<tr>
<td width="232">
<p align="center"><font face="Calibri">indoor parking lot</font></td>
<td>
<p align="center"><font face="Calibri">226</font></td>
</tr>
<tr>
<td width="232">
<p align="center"><font face="Calibri">outdoor normal daylight</font></td>
<td>
<p align="center"><font face="Calibri">546</font></td>
</tr>
<tr>
<td width="232">
<p align="center"><font face="Calibri">outdoor rainy</font></td>
<td>
<p align="center"><font face="Calibri">244</font></td>
</tr>
<tr>
<td width="232">
<p align="center"><font face="Calibri">outdoor shadow</font></td>
<td>
<p align="center"><font face="Calibri">1127</font></td>
</tr>
<tr>
<td width="232">
<p align="center"><font face="Calibri">outdoor street light</font></td>
<td>
<p align="center"><font face="Calibri">147</font></td>
</tr>
<tr>
<td width="232">
<p align="center"><font face="Calibri">outdoor slanted</font></td>
<td>
<p align="center"><font face="Calibri">48</font></td>
</tr>
</table>
<p align="left"><span lang="en-us"><font face="Calibri" style="font-size: 13pt">
We have also developed a matlab labeling tool for labeling parking-slots on
surround-view images, whose interface is shown in the image below. </font>
</span></p>
<p align="left"><img border="0" src="index.1.jpg" width="687" height="458"></p>
<p align="left"><font face="Calibri" style="font-size: 13pt">This labeling tool
and the user instructions can be found here
<a href="https://github.com/Teoge/MarkToolForParkingLotPoint">
https://github.com/Teoge/MarkToolForParkingLotPoint</a>. </font></p>
<hr>
<p><span lang="en-us"><b><font face="Calibri" size="5">Source Codes</font></b></span></p>
<p>
<span lang="en-us"><font face="Calibri" style="font-size: 13pt">Note: all these
codes are implemented in Matlab and can only run on Win64 OS. </font></span></p>
<p>
<span lang="en-us"><font face="Calibri" style="font-size: 13pt">1.
<a href="https://drive.google.com/open?id=1zIY_TNahwXVf5KMHWl06yaXuxx0Ccmeu">MarkingPointDetectionAndPerformanceMeasure.zip</a></font></span></p>
<p>
<span lang="en-us"><font face="Calibri" style="font-size: 13pt">These codes can
fulfill three tasks, detecting marking-points on given surround-view images,
plotting the "missing rates VS FPPI" curve, and calculating the mean
and stand deviation of localization errors. The figure below is the plot of
"missing rates VS FPPI" achieved by our yolo-based marking-point detector.
Its LAMR is 0.35%. </font></span></p>
<p>
<span lang="en-us"><font face="Calibri" style="font-size: 13pt"> <img border="0" src="index.2.jpg" width="517" height="327"></font></span></p>
<p>
<span lang="en-us"><font face="Calibri" style="font-size: 13pt">2.
<a href="https://drive.google.com/open?id=1qPx33fYNY8MhX7hv8lNAHAhlP14aNoDJ">DeepPSMat.zip</a></font></span></p>
<p>
<span lang="en-us"><font face="Calibri" style="font-size: 13pt">System
configurations for running DeepPSDemo: Win10 64bit, Matlab2017a/b, CUDA8.0</font></span></p>
<p>
<span lang="en-us"><font face="Calibri" style="font-size: 13pt">This is the
matlab version for DCNN-based parking-slot detection. The readme file is
<a href="read%20me.pdf">here</a>.</font></span></p>
<p>
<span lang="en-us"><font face="Calibri" style="font-size: 13pt">The
precision-recall rates of our parking-slot detection approach DeepPS on the test
set of the dataset ps2.0 are summarized in the following table, </font></span></p>
<table border="1" width="31%">
<tr>
<td width="232">
<p align="center"><b><font face="Calibri">Subset</font></b></td>
<td>
<p align="center"><b><font face="Calibri">Precision</font></b></td>
<td width="134">
<p align="center"><b><font face="Calibri">Recall</font></b></td>
</tr>
<tr>
<td width="232">
<p align="center"><font face="Calibri">indoor parking lot</font></td>
<td>
<p align="center"><font face="Calibri">99.41%</font></td>
<td width="134">
<p align="center"><font face="Calibri">97.67%</font></td>
</tr>
<tr>
<td width="232">
<p align="center"><font face="Calibri">outdoor normal daylight</font></td>
<td>
<p style="text-align: center; direction: ltr; unicode-bidi: embed; word-break: normal; punctuation-wrap: hanging; margin-left: 0in; margin-top: 0pt; margin-bottom: 0pt">
<font face="Calibri">99.49%</font></td>
<td width="134">
<p style="text-align: center; direction: ltr; unicode-bidi: embed; word-break: normal; punctuation-wrap: hanging; margin-left: 0in; margin-top: 0pt; margin-bottom: 0pt">
<font face="Calibri">99.23%</font></td>
</tr>
<tr>
<td width="232">
<p align="center"><font face="Calibri">outdoor rainy</font></td>
<td>
<p style="text-align: center; direction: ltr; unicode-bidi: embed; word-break: normal; punctuation-wrap: hanging; margin-left: 0in; margin-top: 0pt; margin-bottom: 0pt">
<font face="Calibri">100%</font></td>
<td width="134">
<p style="text-align: center; direction: ltr; unicode-bidi: embed; word-break: normal; punctuation-wrap: hanging; margin-left: 0in; margin-top: 0pt; margin-bottom: 0pt">
<font face="Calibri">99.42%</font></td>
</tr>
<tr>
<td width="232">
<p align="center"><font face="Calibri">outdoor shadow</font></td>
<td>
<p style="text-align: center; direction: ltr; unicode-bidi: embed; word-break: normal; punctuation-wrap: hanging; margin-left: 0in; margin-top: 0pt; margin-bottom: 0pt">
<font face="Calibri">99.86%</font></td>
<td width="134">
<p style="text-align: center; direction: ltr; unicode-bidi: embed; word-break: normal; punctuation-wrap: hanging; margin-left: 0in; margin-top: 0pt; margin-bottom: 0pt">
<font face="Calibri">99.28%</font></td>
</tr>
<tr>
<td width="232">
<p align="center"><font face="Calibri">outdoor street light</font></td>
<td>
<p style="text-align: center; direction: ltr; unicode-bidi: embed; word-break: normal; punctuation-wrap: hanging; margin-left: 0in; margin-top: 0pt; margin-bottom: 0pt">
<font face="Calibri">100%</font></td>
<td width="134">
<p style="text-align: center; direction: ltr; unicode-bidi: embed; word-break: normal; punctuation-wrap: hanging; margin-left: 0in; margin-top: 0pt; margin-bottom: 0pt">
<font face="Calibri">100%</font></td>
</tr>
<tr>
<td width="232">
<p align="center"><font face="Calibri">outdoor slanted</font></td>
<td>
<p style="text-align: center; direction: ltr; unicode-bidi: embed; word-break: normal; punctuation-wrap: hanging; margin-left: 0in; margin-top: 0pt; margin-bottom: 0pt">
<font face="Calibri">96.25%</font></td>
<td width="134">
<p style="text-align: center; direction: ltr; unicode-bidi: embed; word-break: normal; punctuation-wrap: hanging; margin-left: 0in; margin-top: 0pt; margin-bottom: 0pt">
<font face="Calibri">95.06%</font></td>
</tr>
<tr>
<td width="232">
<p align="center"><font face="Calibri">all</font></td>
<td>
<p style="text-align: center; direction: ltr; unicode-bidi: embed; word-break: normal; punctuation-wrap: hanging; margin-left: 0in; margin-top: 0pt; margin-bottom: 0pt">
<font face="Calibri">99.54%</font></td>
<td width="134">
<p style="text-align: center; direction: ltr; unicode-bidi: embed; word-break: normal; punctuation-wrap: hanging; margin-left: 0in; margin-top: 0pt; margin-bottom: 0pt">
<font face="Calibri">98.89%</font></td>
</tr>
</table>
<hr>
<p><span lang="en-us"><b><font face="Calibri" size="5">Demo Videos</font></b></span></p>
<p>
<span lang="en-us"><font face="Calibri" style="font-size: 13pt">The following
are two demo videos demonstrating the capability of our DeepPS approach for
detecting parking-slots. </font></span></p>
<video src="demoPart1.mp4" width="600" height="450" controls preload></video>
<video src="demoPart2.mp4" width="600" height="450" controls preload></video>
<hr>
<p align="justify"><font face="Calibri">Last update: <span lang="en-us">Nov. 23,
</span>201<span lang="en-us">8</span> </font></p>
</body>
</html>