Update README.md

README.md

@@ -1,9 +1,6 @@
----
-license: other
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 # DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes
 
-This repository contains the details of dataset and the Pytorch implementation of Baseline Method CrossMOT of the Paper:
+This repository contains the details of the dataset and the Pytorch implementation of the Baseline Method CrossMOT of the Paper:
 [DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes](https://arxiv.org/abs/2302.07676)
 
 
@@ -14,20 +11,12 @@ Cross-view multi-object tracking aims to link objects between frames and camera
 - **<a href="#des"> <u>Dataset Description</u>**</a>
   - **<a href="#str"> <u>Dataset Structure</u>**</a>
   - **<a href="#dow"> <u>Dataset Downloads</u>**</a>
-- **<a href="#det"> <u>Training Detector</u>**</a>
-- **<a href="#sin"> <u>Single-view Tracking</u>**</a>
-- **<a href="#cro"> <u>Cross-view Tracking</u>**</a>
 - **<a href="#ref"> <u>Reference</u>**</a>
 - **<a href="#con"> <u>Contact</u>**</a>
 
-The test result of the cross-view MOT baseline method *MvMHAT* on the DIVOTrack. 
-![test.gif](asset/test.gif)
-
-The ground truth of the DIVOTrack.
-![gt.gif](asset/gt.gif)
 
 ## <a id="des">Dataset Description</a>
-We collect data in 10 different real-world scenarios, named: `'Circle', 'Shop', 'Moving', 'Park', 'Ground', 'Gate1', 'Floor', 'Side', 'Square', 'Gate2'`. All
+We collect data in 10 different real-world scenarios, named: `'Circle', 'Shop', 'Moving', 'Park', 'Ground', 'Gate1', 'Floor', 'Side', 'Square', and 'Gate2'`. All
 the sequences are captured by using 3 moving cameras: `'View1', 'View2', 'View3'` and are manually synchronized. 
 
 In the old version, the corresponding scenarios named: `'circleRegion', 'innerShop', 'movingView', 'park', 'playground', 'shopFrontGate', 'shopSecondFloor', 'shopSideGate', 'shopSideSquare', 'southGate'`. The corresponding camera named: `'Drone', 'View1', 'View2'`.
@@ -54,36 +43,7 @@ DIVOTrack
 
 ```
 ### <a id="dow">Dataset Downloads</a>
-The whole dataset can download from [GoogleDrive](https://drive.google.com/drive/folders/1RCk95TdFv3Tt7gVuyxJasiHG1IPE6jkX?usp=sharing). **Note that, each file needs to unzip by the password. You can decompress each `.zip` file in its folder after send us (shengyuhao@zju.edu.cn, gaoangwang@intl.zju.edu.cn) the License in any format.** After that, you should run `generate_ini.py` to generate `seqinfo.ini` file. 
-
-## <a id="det">Training Detector</a>
-The training process of our detector is in `./Training_detector/` and the details can see from  [Training_detector/README.md](https://github.com/shengyuhao/DIVOTrack/tree/main/Training_Detector#readme).
-## <a id="sin">Single-view Tracking</a>
-We conducted experiments on DIVOTrack in fix benchmarks:
-
-| Benchmark | HOTA ↑ | IDF1 ↑ | MOTA ↑ | MOTP ↑ | MT ↑ | ML ↓ | AssA ↑ | IDSw ↓ | FM ↓ |
-| --------- | ------ | ------ | ------ | ------ | ---- | ---- | ------ | ------ | ---- |
-| [DeepSort](./Single_view_Tracking/Deepsort/)       | 54.3 | 59.9 | 79.6 | 81.2 | 462 | 50 | 45.0 | 1,920 | **2,504** |
-| [CenterTrack](./Single_view_Tracking/CenterTrack/) | 55.3 | 62.2 | 73.4 | 80.6 | **534** | 35 | 49.2 | 1,631 | 2,950 |
-| [Tracktor](./Single_view_Tracking/Tracktor/)       | 48.4 | 56.2 | 66.6 | 80.8 | 517 | **22** | 40.3 | 1,382 | 3,337 |
-| [FairMOT](./Single_view_Tracking/FairMOT/)         | 65.3 | 78.2 | 82.7 | 81.9 | 486 | 48 | **62.7** | 731   | 3,498 |
-| [TraDeS](./Single_view_Tracking/TraDeS/)           | 58.9 | 67.3 | 74.2 | **82.3** | 504 | 38 | 54.0 | 1,263 | 2,647 |
-
-Each single-view tracking baseline is evaluated using [TrackEval](https://github.com/shengyuhao/DIVOTrack/tree/main/TrackEval#readme).
-
-## <a id="cro">Cross-view Tracking</a>
-We conducted experiments on the DIVOTrack dataset using six benchmarks as well as our proposed method [CrossMOT](./CrossMOT/)
-| Benchmark | CVMA ↑ | CVIDF1 ↑ |
-| --------- | ------ | -------- |
-| [OSNet](./Cross_view_Tracking/OSNet/)       | 34.3 | 46.0 |
-| [Strong](./Cross_view_Tracking/StrongReID/) | 40.9 | 45.9 |
-| [AGW](./Cross_view_Tracking/AGW/)           | 57.0 | 56.8 |
-| [MvMHAT](./Cross_view_Tracking/MvMHAT/)     | 61.0 | 62.6 |
-| [CT](./Cross_view_Tracking/CT/)             | 64.9 | 65.0 |
-| [MGN](./Cross_view_Tracking/MGN/)           | 33.5 | 39.4 |
-| [CrossMOT](./CrossMOT/)                     | **72.4** | **71.1** |
-
-With the exception of CrossMOT, all of the other Re-ID methods require [Multi_view_Tracking](https://github.com/shengyuhao/DIVOTrack/tree/main/Multi_view_Tracking#readme) to predict the tracking results after they are obtained. Finally, the results of CVMA and CVIDF1 are obtained through [MOTChallengeEvalKit_cv_test](https://github.com/shengyuhao/DIVOTrack/tree/main/MOTChallengeEvalKit_cv_test#readme).
+The whole dataset can be downloaded from [GoogleDrive](https://drive.google.com/drive/folders/1RCk95TdFv3Tt7gVuyxJasiHG1IPE6jkX?usp=sharing). **Note that, each file needs to unzip by the password. You can decompress each `.zip` file in its folder after send us (shengyuhao@zju.edu.cn, gaoangwang@intl.zju.edu.cn) the License in any format.** After that, you should run `generate_ini.py` to generate `seqinfo.ini` file. 
 
 
 ## <a id="ref">Reference</a>