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12 KiB
Markdown
304 lines
12 KiB
Markdown
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简体中文 | [English](GETTING_STARTED.md)
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# 目录
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## 多目标跟踪数据集准备
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- [MOT数据集](#MOT数据集)
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- [数据格式](#数据格式)
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- [数据集目录](#数据集目录)
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- [下载链接](#下载链接)
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- [用户数据准备](#用户数据准备)
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- [引用](#引用)
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### MOT数据集
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PaddleDetection使用和[JDE](https://github.com/Zhongdao/Towards-Realtime-MOT) 还有[FairMOT](https://github.com/ifzhang/FairMOT)相同的数据集，请先下载并准备好所有的数据集包括**Caltech Pedestrian, CityPersons, CUHK-SYSU, PRW, ETHZ, MOT17和MOT16**。此外还可以下载**MOT15和MOT20**数据集，如果您想使用这些数据集，请**遵循他们的License**。
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### 数据格式
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这几个相关数据集都遵循以下结构：
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```
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Caltech
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|——————images
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| └——————00001.jpg
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| |—————— ...
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| └——————0000N.jpg
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└——————labels_with_ids
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└——————00001.txt
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|—————— ...
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└——————0000N.txt
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MOT17
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|——————images
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| └——————train
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| └——————test
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└——————labels_with_ids
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└——————train
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```
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所有数据集的标注是以统一数据格式提供的。各个数据集中每张图片都有相应的标注文本。给定一个图像路径，可以通过将字符串`images`替换为`labels_with_ids`并将`.jpg`替换为`.txt`来生成标注文本路径。在标注文本中，每行都描述一个边界框，格式如下：
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```
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[class] [identity] [x_center] [y_center] [width] [height]
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```
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**注意**:
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- `class`为`0`，目前仅支持单类别多目标跟踪。
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- `identity`是从`0`到`num_identifies-1`的整数(`num_identifies`是数据集中不同物体实例的总数)，如果此框没有`identity`标注，则为`-1`。
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- `[x_center] [y_center] [width] [height]`是中心点坐标和宽高，注意它们的值是由图片的宽度/高度标准化的，因此它们是从0到1的浮点数。
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### 数据集目录
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首先按照以下命令下载image_lists.zip并解压放在`dataset/mot`目录下：
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```
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wget https://dataset.bj.bcebos.com/mot/image_lists.zip
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```
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然后依次下载各个数据集并解压，最终目录为：
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```
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dataset/mot
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|——————image_lists
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|——————caltech.10k.val
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|——————caltech.all
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|——————caltech.train
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|——————caltech.val
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|——————citypersons.train
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|——————citypersons.val
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|——————cuhksysu.train
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|——————cuhksysu.val
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|——————eth.train
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|——————mot15.train
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|——————mot16.train
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|——————mot17.train
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|——————mot20.train
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|——————prw.train
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|——————prw.val
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|——————Caltech
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|——————Cityscapes
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|——————CUHKSYSU
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|——————ETHZ
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|——————MOT15
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|——————MOT16
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|——————MOT17
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|——————MOT20
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|——————PRW
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```
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### 下载链接
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#### Caltech Pedestrian
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Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/1sYBXXvQaXZ8TuNwQxMcAgg)
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[[1]](https://pan.baidu.com/s/1lVO7YBzagex1xlzqPksaPw)
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[[2]](https://pan.baidu.com/s/1PZXxxy_lrswaqTVg0GuHWg)
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[[3]](https://pan.baidu.com/s/1M93NCo_E6naeYPpykmaNgA)
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[[4]](https://pan.baidu.com/s/1ZXCdPNXfwbxQ4xCbVu5Dtw)
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[[5]](https://pan.baidu.com/s/1kcZkh1tcEiBEJqnDtYuejg)
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[[6]](https://pan.baidu.com/s/1sDjhtgdFrzR60KKxSjNb2A)
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[[7]](https://pan.baidu.com/s/18Zvp_d33qj1pmutFDUbJyw)
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Google Drive: [[annotations]](https://drive.google.com/file/d/1h8vxl_6tgi9QVYoer9XcY9YwNB32TE5k/view?usp=sharing),
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请从[这个页面](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/datasets/USA/)下载所有的`.tar`结尾的图片文件, 并解压到`Caltech/images`目录。
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你需要使用这个[工具](https://github.com/mitmul/caltech-pedestrian-dataset-converter) 将原始数据格式转换为jpeg图像。
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原始数据集网址: [CaltechPedestrians](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/)
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#### CityPersons
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Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/1g24doGOdkKqmbgbJf03vsw)
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[[1]](https://pan.baidu.com/s/1mqDF9M5MdD3MGxSfe0ENsA)
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[[2]](https://pan.baidu.com/s/1Qrbh9lQUaEORCIlfI25wdA)
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[[3]](https://pan.baidu.com/s/1lw7shaffBgARDuk8mkkHhw)
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Google Drive:
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[[0]](https://drive.google.com/file/d/1DgLHqEkQUOj63mCrS_0UGFEM9BG8sIZs/view?usp=sharing)
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[[1]](https://drive.google.com/file/d/1BH9Xz59UImIGUdYwUR-cnP1g7Ton_LcZ/view?usp=sharing)
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[[2]](https://drive.google.com/file/d/1q_OltirP68YFvRWgYkBHLEFSUayjkKYE/view?usp=sharing)
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[[3]](https://drive.google.com/file/d/1VSL0SFoQxPXnIdBamOZJzHrHJ1N2gsTW/view?usp=sharing)
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原始数据集网址: [Citypersons pedestrian detection dataset](https://github.com/cvgroup-njust/CityPersons)
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#### CUHK-SYSU
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Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/1YFrlyB1WjcQmFW3Vt_sEaQ)
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Google Drive:
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[[0]](https://drive.google.com/file/d/1D7VL43kIV9uJrdSCYl53j89RE2K-IoQA/view?usp=sharing)
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原始数据集网址: [CUHK-SYSU Person Search Dataset](http://www.ee.cuhk.edu.hk/~xgwang/PS/dataset.html)
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#### PRW
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Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/1iqOVKO57dL53OI1KOmWeGQ)
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Google Drive:
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[[0]](https://drive.google.com/file/d/116_mIdjgB-WJXGe8RYJDWxlFnc_4sqS8/view?usp=sharing)
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#### ETHZ (overlapping videos with MOT-16 removed):
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Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/14EauGb2nLrcB3GRSlQ4K9Q)
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Google Drive:
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[[0]](https://drive.google.com/file/d/19QyGOCqn8K_rc9TXJ8UwLSxCx17e0GoY/view?usp=sharing)
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原始数据集网址: [ETHZ pedestrian datset](https://data.vision.ee.ethz.ch/cvl/aess/dataset/)
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#### MOT-17
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Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/1lHa6UagcosRBz-_Y308GvQ)
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Google Drive:
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[[0]](https://drive.google.com/file/d/1ET-6w12yHNo8DKevOVgK1dBlYs739e_3/view?usp=sharing)
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原始数据集网址: [MOT-17](https://motchallenge.net/data/MOT17/)
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#### MOT-16
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Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/10pUuB32Hro-h-KUZv8duiw)
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Google Drive:
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[[0]](https://drive.google.com/file/d/1254q3ruzBzgn4LUejDVsCtT05SIEieQg/view?usp=sharing)
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原始数据集网址: [MOT-16](https://motchallenge.net/data/MOT16/)
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#### MOT-15
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原始数据集网址: [MOT-15](https://motchallenge.net/data/MOT15/)
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#### MOT-20
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原始数据集网址: [MOT-20](https://motchallenge.net/data/MOT20/)
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### 用户数据准备
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为了规范地进行训练和评测，用户数据需要转成和MOT-16数据集相同的目录和格式：
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```
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custom_data
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|——————images
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| └——————test
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| └——————train
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| └——————seq1
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| | └——————gt
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| | | └——————gt.txt
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| | └——————img1
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| | | └——————000001.jpg
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| | | |——————000002.jpg
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| | | └—————— ...
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| | └——————seqinfo.ini
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| └——————seq2
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| └——————...
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└——————labels_with_ids
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└——————train
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└——————seq1
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| └——————000001.txt
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| |——————000002.txt
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| └—————— ...
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└——————seq2
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└—————— ...
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```
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#### images文件夹
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- `gt.txt`是原始标注文件，而训练所用标注是`labels_with_ids`文件夹。
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- `img1`文件夹里是按照一定帧率抽好的图片。
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- `seqinfo.ini`文件是视频信息描述文件，需要如下格式的信息：
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```
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[Sequence]
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name=MOT16-02
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imDir=img1
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frameRate=30
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seqLength=600
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imWidth=1920
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imHeight=1080
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imExt=.jpg
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```
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`gt.txt`里是当前视频中所有图片的原始标注文件，每行都描述一个边界框，格式如下：
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```
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[frame_id],[identity],[bb_left],[bb_top],[width],[height],[x],[y],[z]
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```
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**注意**:
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- `frame_id`为当前图片帧序号
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- `identity`是从`0`到`num_identifies-1`的整数(`num_identifies`是数据集中不同物体实例的总数)，如果此框没有`identity`标注，则为`-1`
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- `bb_left`是目标框的左边界的x坐标
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- `bb_top`是目标框的上边界的y坐标
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- `width，height`是真实的像素宽高
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- `x,y,z`是3D中用到的，在2D中默认为`-1`
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#### labels_with_ids文件夹
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所有数据集的标注是以统一数据格式提供的。各个数据集中每张图片都有相应的标注文本。给定一个图像路径，可以通过将字符串`images`替换为`labels_with_ids`并将`.jpg`替换为`.txt`来生成标注文本路径。在标注文本中，每行都描述一个边界框，格式如下：
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```
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[class] [identity] [x_center] [y_center] [width] [height]
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```
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**注意**:
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- `class`为`0`，目前仅支持单类别多目标跟踪。
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- `identity`是从`0`到`num_identifies-1`的整数(`num_identifies`是数据集中不同物体实例的总数)，如果此框没有`identity`标注，则为`-1`。
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- `[x_center] [y_center] [width] [height]`是中心点坐标和宽高，注意是由图片的宽度/高度标准化的，因此它们是从0到1的浮点数。
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可采用如下脚本生成相应的`labels_with_ids`:
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```
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cd dataset/mot
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python gen_labels_MOT.py
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```
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### 引用
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Caltech:
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```
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@inproceedings{ dollarCVPR09peds,
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author = "P. Doll\'ar and C. Wojek and B. Schiele and P. Perona",
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title = "Pedestrian Detection: A Benchmark",
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booktitle = "CVPR",
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month = "June",
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year = "2009",
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city = "Miami",
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}
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```
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Citypersons:
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```
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@INPROCEEDINGS{Shanshan2017CVPR,
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Author = {Shanshan Zhang and Rodrigo Benenson and Bernt Schiele},
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Title = {CityPersons: A Diverse Dataset for Pedestrian Detection},
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Booktitle = {CVPR},
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Year = {2017}
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}
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@INPROCEEDINGS{Cordts2016Cityscapes,
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title={The Cityscapes Dataset for Semantic Urban Scene Understanding},
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author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt},
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booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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year={2016}
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}
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```
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CUHK-SYSU:
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```
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@inproceedings{xiaoli2017joint,
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title={Joint Detection and Identification Feature Learning for Person Search},
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author={Xiao, Tong and Li, Shuang and Wang, Bochao and Lin, Liang and Wang, Xiaogang},
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booktitle={CVPR},
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year={2017}
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}
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```
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PRW:
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```
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@inproceedings{zheng2017person,
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title={Person re-identification in the wild},
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author={Zheng, Liang and Zhang, Hengheng and Sun, Shaoyan and Chandraker, Manmohan and Yang, Yi and Tian, Qi},
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booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
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pages={1367--1376},
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year={2017}
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}
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```
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ETHZ:
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```
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@InProceedings{eth_biwi_00534,
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author = {A. Ess and B. Leibe and K. Schindler and and L. van Gool},
|
|||
|
|
title = {A Mobile Vision System for Robust Multi-Person Tracking},
|
|||
|
|
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08)},
|
|||
|
|
year = {2008},
|
|||
|
|
month = {June},
|
|||
|
|
publisher = {IEEE Press},
|
|||
|
|
keywords = {}
|
|||
|
|
}
|
|||
|
|
```
|
|||
|
|
MOT-16&17:
|
|||
|
|
```
|
|||
|
|
@article{milan2016mot16,
|
|||
|
|
title={MOT16: A benchmark for multi-object tracking},
|
|||
|
|
author={Milan, Anton and Leal-Taix{\'e}, Laura and Reid, Ian and Roth, Stefan and Schindler, Konrad},
|
|||
|
|
journal={arXiv preprint arXiv:1603.00831},
|
|||
|
|
year={2016}
|
|||
|
|
}
|
|||
|
|
```
|