[MOT] add deepsort video infer and doc (#3346)

* add deepsort video infer modelzoo, test=document_fix * fix deepsort doc results, test=document_fix

[MOT] add deepsort video infer and doc (#3346)
* add deepsort video infer modelzoo, test=document_fix * fix deepsort doc results, test=document_fix
bfc439ce · George Ni · GitHub · 37962dcb · bfc439ce · bfc439ce
6 changed file
--- a/configs/mot/README.md
+++ b/configs/mot/README.md
@@ -45,20 +45,24 @@ pip install -r requirements.txt

 ## Model Zoo

-### DeepSORT on MOT-16 Training Set
+### DeepSORT Results on MOT-16 Training Set

-| backbone  | input shape | MOTA | IDF1 |  IDS  |   FP  |   FN  |   FPS  | download| config |
-| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :---: | :---: |
-| ResNet-101 | 1088x608 |  72.2  |  60.5  | 998  |  8054  | 21644 |  - | [download](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+| backbone  | input shape | MOTA | IDF1 |  IDS  |   FP  |   FN  |   FPS  | det result/model |ReID model| config |
+| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :---: | :---: | :---: |
+| ResNet-101 | 1088x608 |  72.2  |  60.5  | 998  |  8054  | 21644 |  - | [det result](https://dataset.bj.bcebos.com/mot/det_results_dir.zip) |[ReID model](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+| ResNet-101 | 1088x608 |  68.3  |  56.5  | 1722 |  17337 | 15890 |  - | [det model](https://paddledet.bj.bcebos.com/models/mot/jde_yolov3_darknet53_30e_1088x608.pdparams) |[ReID model](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |

-### DeepSORT on MOT-16 Test Set
+### DeepSORT Results on MOT-16 Test Set

-| backbone  | input shape | MOTA | IDF1 |  IDS  |   FP  |   FN  |   FPS  | download| config |
-| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :---: | :---: |
-| ResNet-101 | 1088x608 |  64.1  |  53.0  | 1024  |  12457  | 51919 |  - | [download](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+| backbone  | input shape | MOTA | IDF1 |  IDS  |   FP  |   FN  |   FPS  | det result/model |ReID model| config |
+| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :---: | :---: | :---: |
+| ResNet-101 | 1088x608 |  64.1  |  53.0  | 1024  |  12457  | 51919 |  - |[det result](https://dataset.bj.bcebos.com/mot/det_results_dir.zip) |[ReID model](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+| ResNet-101 | 1088x608 |  61.2  |  48.5  | 1799  |  25796  | 43232 |  - | [det model](https://paddledet.bj.bcebos.com/models/mot/jde_yolov3_darknet53_30e_1088x608.pdparams)  |[ReID model](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |

 **Notes:**
- DeepSORT does not need to train on MOT dataset, only used for evaluation. Before DeepSORT evaluation, you should get detection results by a detection model first, and then prepare them like this:
+DeepSORT does not need to train on MOT dataset, only used for evaluation. Now it supports two evaluation methods.
+
+- 1.Load the result file and the ReID model. Before DeepSORT evaluation, you should get detection results by a detection model first, and then prepare them like this:
 ```
 det_results_dir
   |——————MOT16-02.txt
@@ -69,24 +73,24 @@ det_results_dir
   |——————MOT16-11.txt
   |——————MOT16-13.txt
 ```
-For MOT16 dataset, you can download the det_results_dir.zip of one detection model provided by PaddleDetection：
+For MOT16 dataset, you can download a detection result after matching called det_results_dir.zip provided by PaddleDetection：
 ```
 wget https://dataset.bj.bcebos.com/mot/det_results_dir.zip
 ```
-Each txt is the detection result of all the pictures extracted from each video, and each line describes a bounding box with the following format:
+If you use a stronger detection model, you can get better results. Each txt is the detection result of all the pictures extracted from each video, and each line describes a bounding box with the following format:
 ```
-[frame_id],[identity],[bb_left],[bb_top],[width],[height],[conf]
+[frame_id],[bb_left],[bb_top],[width],[height],[conf]
 ```
-**Notes:**
 - `frame_id` is the frame number of the image
- `identity` is the object id using default value `-1`
 - `bb_left` is the X coordinate of the left bound of the object box
 - `bb_top` is the Y coordinate of the upper bound of the object box
 - `width,height` is the pixel width and height
 - `conf` is the object score with default value `1` (the results had been filtered out according to the detection score threshold)

+- 2.Load the detection model and the ReID model at the same time. Here, the JDE version of YOLOv3 is selected. For more detail of configuration, see `configs/mot/deepsort/_base_/deepsort_yolov3_darknet53_pcb_pyramid_r101.yml`.
+

-### JDE on MOT-16 Training Set
+### JDE Results on MOT-16 Training Set

 | backbone           | input shape | MOTA | IDF1  |  IDS  |   FP  |  FN  |  FPS  | download | config |
 | :----------------- | :------- | :----: | :----: | :---: | :----: | :---: | :---: | :---: | :---: |
@@ -94,7 +98,7 @@ Each txt is the detection result of all the pictures extracted from each video,
 | DarkNet53          | 864x480 |  69.1  |  64.7  | 1539  |  7544  | 25046 |   -   |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_864x480.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_864x480.yml) |
 | DarkNet53          | 576x320 |  63.7  |  64.4  | 1310  |  6782  | 31964 |   -   |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_576x320.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_576x320.yml) |

-### JDE on MOT-16 Test Set
+### JDE Results on MOT-16 Test Set

 | backbone           | input shape | MOTA | IDF1  |  IDS  |   FP  |  FN  |  FPS  | download | config |
 | :----------------- | :------- | :----: | :----: | :---: | :----: | :---: | :---: | :---: | :---: |

--- a/configs/mot/README_cn.md
+++ b/configs/mot/README_cn.md
@@ -46,18 +46,23 @@ pip install -r requirements.txt

 ### DeepSORT在MOT-16 Training Set上结果

-|  骨干网络  |  输入尺寸  |  MOTA  |  IDF1  |  IDS |   FP   |   FN  |  FPS | 下载链接 | 配置文件 |
-| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :-----: | :-----: |
-| ResNet-101 | 1088x608 |  72.2  |  60.5  | 998  |  8054  | 21644 |  - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+|  骨干网络  |  输入尺寸  |  MOTA  |  IDF1  |  IDS |   FP   |   FN  |  FPS | 检测结果或模型 | ReID模型 |配置文件 |
+| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :-----:| :-----: | :-----: |
+| ResNet-101 | 1088x608 |  72.2  |  60.5  | 998  |  8054  | 21644 |  - | [检测结果](https://dataset.bj.bcebos.com/mot/det_results_dir.zip) |[ReID模型](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+| ResNet-101 | 1088x608 |  68.3  |  56.5  | 1722 |  17337 | 15890 |  - | [检测模型](https://paddledet.bj.bcebos.com/models/mot/jde_yolov3_darknet53_30e_1088x608.pdparams) |[ReID模型](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |

 ### DeepSORT在MOT-16 Test Set上结果

-|  骨干网络  |  输入尺寸  |  MOTA  |  IDF1  |  IDS |   FP   |   FN  |  FPS | 下载链接 | 配置文件 |
-| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :-----: | :-----: |
-| ResNet-101 | 1088x608 |  64.1  |  53.0  | 1024  |  12457  | 51919 |  - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+|  骨干网络  |  输入尺寸  |  MOTA  |  IDF1  |  IDS |   FP   |   FN  |  FPS | 检测结果或模型 | ReID模型 |配置文件 |
+| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :-----: | :-----: |:-----: |
+| ResNet-101 | 1088x608 |  64.1  |  53.0  | 1024  |  12457  | 51919 |  - | [检测结果](https://dataset.bj.bcebos.com/mot/det_results_dir.zip) | [ReID模型](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+| ResNet-101 | 1088x608 |  61.2  |  48.5  | 1799  |  25796  | 43232 |  - | [检测模型](https://paddledet.bj.bcebos.com/models/mot/jde_yolov3_darknet53_30e_1088x608.pdparams)  |[ReID模型](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |

 **注意:**
-  DeepSORT不需要训练MOT数据集，只用于评估。在使用DeepSORT模型评估之前，应该首先通过一个检测模型得到检测结果，然后像这样准备好结果文件:
+
+DeepSORT不需要训练MOT数据集，只用于评估，现在支持两种评估的方式。
+
+- 第1种方式是加载检测结果文件和ReID模型，在使用DeepSORT模型评估之前，应该首先通过一个检测模型得到检测结果，然后像这样准备好结果文件:
 ```
 det_results_dir
   |——————MOT16-02.txt
@@ -68,22 +73,22 @@ det_results_dir
   |——————MOT16-11.txt
   |——————MOT16-13.txt
 ```
-对于MOT16数据集，可以下载PaddleDetection提供的一个检测结果det_results_dir.zip并解压：
+对于MOT16数据集，可以下载PaddleDetection提供的一个经过匹配之后的检测框结果det_results_dir.zip并解压：
 ```
 wget https://dataset.bj.bcebos.com/mot/det_results_dir.zip
 ```
-其中每个txt是每个视频中所有图片的检测结果，每行都描述一个边界框，格式如下：
+如果使用更强的检测模型，可以取得更好的结果。其中每个txt是每个视频中所有图片的检测结果，每行都描述一个边界框，格式如下：
 ```
-[frame_id],[identity],[bb_left],[bb_top],[width],[height],[conf]
+[frame_id],[bb_left],[bb_top],[width],[height],[conf]
 ```
-**注意**:
 - `frame_id`是图片帧的序号
- `identity`是目标id采用默认值为`-1`
 - `bb_left`是目标框的左边界的x坐标
 - `bb_top`是目标框的上边界的y坐标
 - `width,height`是真实的像素宽高
 - `conf`是目标得分设置为`1`(已经按检测的得分阈值筛选出的检测结果)

+- 第2种方式是同时加载检测模型和ReID模型，此处选用JDE版本的YOLOv3，具体配置见`configs/mot/deepsort/_base_/deepsort_yolov3_darknet53_pcb_pyramid_r101.yml`
+

 ### JDE在MOT-16 Training Set上结果


--- a/configs/mot/deepsort/README.md
+++ b/configs/mot/deepsort/README.md
@@ -13,20 +13,24 @@ English | [简体中文](README_cn.md)

 ## Model Zoo

-### DeepSORT on MOT-16 Training Set
+### DeepSORT Results on MOT-16 Training Set

-| backbone  | input shape | MOTA | IDF1 |  IDS  |   FP  |   FN  |   FPS  | download| config |
-| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :---: | :---: |
-| ResNet101 | 1088x608 |  72.2  |  60.5  | 998  |  8054  | 21644 |  - | [download](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+| backbone  | input shape | MOTA | IDF1 |  IDS  |   FP  |   FN  |   FPS  | det result/model |ReID model| config |
+| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :---: | :---: | :---: |
+| ResNet-101 | 1088x608 |  72.2  |  60.5  | 998  |  8054  | 21644 |  - | [det result](https://dataset.bj.bcebos.com/mot/det_results_dir.zip) |[ReID model](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+| ResNet-101 | 1088x608 |  68.3  |  56.5  | 1722 |  17337 | 15890 |  - | [det model](https://paddledet.bj.bcebos.com/models/mot/jde_yolov3_darknet53_30e_1088x608.pdparams) |[ReID model](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |

-### DeepSORT on MOT-16 Test Set
+### DeepSORT Results on MOT-16 Test Set

-| backbone  | input shape | MOTA | IDF1 |  IDS  |   FP  |   FN  |   FPS  | download| config |
-| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :---: | :---: |
-| ResNet101 | 1088x608 |  64.1  |  53.0  | 1024  |  12457  | 51919 |  - | [download](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+| backbone  | input shape | MOTA | IDF1 |  IDS  |   FP  |   FN  |   FPS  | det result/model |ReID model| config |
+| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :---: | :---: | :---: |
+| ResNet-101 | 1088x608 |  64.1  |  53.0  | 1024  |  12457  | 51919 |  - |[det result](https://dataset.bj.bcebos.com/mot/det_results_dir.zip) |[ReID model](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+| ResNet-101 | 1088x608 |  61.2  |  48.5  | 1799  |  25796  | 43232 |  - | [det model](https://paddledet.bj.bcebos.com/models/mot/jde_yolov3_darknet53_30e_1088x608.pdparams)  |[ReID model](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |

 **Notes:**
- DeepSORT does not need to train on MOT dataset, only used for evaluation. Before DeepSORT evaluation, you should get detection results by a detection model first, and then prepare them like this:
+DeepSORT does not need to train on MOT dataset, only used for evaluation. Now it supports two evaluation methods.
+
+- 1.Load the result file and the ReID model. Before DeepSORT evaluation, you should get detection results by a detection model first, and then prepare them like this:
 ```
 det_results_dir
   |——————MOT16-02.txt
@@ -37,30 +41,44 @@ det_results_dir
   |——————MOT16-11.txt
   |——————MOT16-13.txt
 ```
-For MOT16 dataset, you can download the det_results_dir.zip provided by PaddleDetection：
+For MOT16 dataset, you can download a detection result after matching called det_results_dir.zip provided by PaddleDetection：
 ```
 wget https://dataset.bj.bcebos.com/mot/det_results_dir.zip
 ```
-Each txt is the detection result of all the pictures extracted from each video, and each line describes a bounding box with the following format:
+If you use a stronger detection model, you can get better results. Each txt is the detection result of all the pictures extracted from each video, and each line describes a bounding box with the following format:
 ```
-[frame_id],[identity],[bb_left],[bb_top],[width],[height],[conf]
+[frame_id],[bb_left],[bb_top],[width],[height],[conf]
 ```
-**Notes:**
 - `frame_id` is the frame number of the image
- `identity` is the object id using default value `-1`
 - `bb_left` is the X coordinate of the left bound of the object box
 - `bb_top` is the Y coordinate of the upper bound of the object box
 - `width,height` is the pixel width and height
 - `conf` is the object score with default value `1` (the results had been filtered out according to the detection score threshold)

+- 2.Load the detection model and the ReID model at the same time. Here, the JDE version of YOLOv3 is selected. For more detail of configuration, see `configs/mot/deepsort/_base_/deepsort_yolov3_darknet53_pcb_pyramid_r101.yml`.
+
 ## Getting Start

 ### 1. Evaluation

 ```bash
-# track the objects by loading detected result files
+# Load the result file and ReID model to get the tracking result
 CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml --det_results_dir {your detection results}
+
+# Load the detection model and ReID model to get the tracking results
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/deepsort_yolov3_pcb_pyramid_r101.yml
+```
+
+### 2. Inference
+
+Inference a vidoe on single GPU with following command:
+
+```bash
+# inference on video and save a video
+CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py -c configs/mot/deepsort/deepsort_yolov3_pcb_pyramid_r101.yml --video_file={your video name}.mp4  --save_videos
 ```
+**Notes:**
+ Please make sure that [ffmpeg](https://ffmpeg.org/ffmpeg.html) is installed first, on Linux(Ubuntu) platform you can directly install it by the following command:`apt-get update && apt-get install -y ffmpeg`.

 ## Citations
 ```

--- a/configs/mot/deepsort/README_cn.md
+++ b/configs/mot/deepsort/README_cn.md
@@ -15,18 +15,23 @@

 ### DeepSORT在MOT-16 Training Set上结果

-|  骨干网络  |  输入尺寸  |  MOTA  |  IDF1  |  IDS |   FP   |   FN  |  FPS | 下载链接 | 配置文件 |
-| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :-----: | :-----: |
-| ResNet101 | 1088x608 |  72.2  |  60.5  | 998  |  8054  | 21644 |  - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+|  骨干网络  |  输入尺寸  |  MOTA  |  IDF1  |  IDS |   FP   |   FN  |  FPS | 检测结果或模型 | ReID模型 |配置文件 |
+| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :-----:| :-----: | :-----: |
+| ResNet-101 | 1088x608 |  72.2  |  60.5  | 998  |  8054  | 21644 |  - | [检测结果](https://dataset.bj.bcebos.com/mot/det_results_dir.zip) |[ReID模型](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+| ResNet-101 | 1088x608 |  68.3  |  56.5  | 1722 |  17337 | 15890 |  - | [检测模型](https://paddledet.bj.bcebos.com/models/mot/jde_yolov3_darknet53_30e_1088x608.pdparams) |[ReID模型](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |

 ### DeepSORT在MOT-16 Test Set上结果

-|  骨干网络  |  输入尺寸  |  MOTA  |  IDF1  |  IDS |   FP   |   FN  |  FPS | 下载链接 | 配置文件 |
-| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :-----: | :-----: |
-| ResNet101 | 1088x608 |  64.1  |  53.0  | 1024  |  12457  | 51919 |  - | [下载链接](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+|  骨干网络  |  输入尺寸  |  MOTA  |  IDF1  |  IDS |   FP   |   FN  |  FPS | 检测结果或模型 | ReID模型 |配置文件 |
+| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: | :-----: | :-----: |:-----: |
+| ResNet-101 | 1088x608 |  64.1  |  53.0  | 1024  |  12457  | 51919 |  - | [检测结果](https://dataset.bj.bcebos.com/mot/det_results_dir.zip) | [ReID模型](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+| ResNet-101 | 1088x608 |  61.2  |  48.5  | 1799  |  25796  | 43232 |  - | [检测模型](https://paddledet.bj.bcebos.com/models/mot/jde_yolov3_darknet53_30e_1088x608.pdparams)  |[ReID模型](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |

 **注意:**
-  DeepSORT不需要训练MOT数据集，只用于评估。在使用DeepSORT模型评估之前，应该首先通过一个检测模型得到检测结果，然后像这样准备好结果文件:
+
+DeepSORT不需要训练MOT数据集，只用于评估，现在支持两种评估的方式。
+
+- 第1种方式是加载检测结果文件和ReID模型，在使用DeepSORT模型评估之前，应该首先通过一个检测模型得到检测结果，然后像这样准备好结果文件:
 ```
 det_results_dir
   |——————MOT16-02.txt
@@ -37,31 +42,46 @@ det_results_dir
   |——————MOT16-11.txt
   |——————MOT16-13.txt
 ```
-对于MOT16数据集，可以下载PaddleDetection提供的det_results_dir.zip并解压：
+对于MOT16数据集，可以下载PaddleDetection提供的一个经过匹配之后的检测框结果det_results_dir.zip并解压：
 ```
 wget https://dataset.bj.bcebos.com/mot/det_results_dir.zip
 ```
-其中每个txt是每个视频中所有图片的检测结果，每行都描述一个边界框，格式如下：
+如果使用更强的检测模型，可以取得更好的结果。其中每个txt是每个视频中所有图片的检测结果，每行都描述一个边界框，格式如下：
 ```
-[frame_id],[identity],[bb_left],[bb_top],[width],[height],[conf]
+[frame_id],[bb_left],[bb_top],[width],[height],[conf]
 ```
-**注意**:
 - `frame_id`是图片帧的序号
- `identity`是目标id采用默认值为`-1`
 - `bb_left`是目标框的左边界的x坐标
 - `bb_top`是目标框的上边界的y坐标
 - `width,height`是真实的像素宽高
 - `conf`是目标得分设置为`1`(已经按检测的得分阈值筛选出的检测结果)

+- 第2种方式是同时加载检测模型和ReID模型，此处选用JDE版本的YOLOv3，具体配置见`configs/mot/deepsort/_base_/deepsort_yolov3_darknet53_pcb_pyramid_r101.yml`
+
 ## 快速开始

 ### 1. 评估

 ```bash
-# 加载检测结果文件得到跟踪结果
+# 加载检测结果文件和ReID模型，得到跟踪结果
 CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml --det_results_dir {your detection results}
+
+# 加载检测模型和ReID模型，得到跟踪结果
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/deepsort_yolov3_pcb_pyramid_r101.yml
 ```

+### 2. 预测
+
+使用单个GPU通过如下命令预测一个视频，并保存为视频
+
+```bash
+# 加载检测模型和ReID模型，得到跟踪结果
+CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py -c configs/mot/deepsort/deepsort_yolov3_pcb_pyramid_r101.yml --video_file={your video name}.mp4  --save_videos
+```
+
+**注意:**
+ 请先确保已经安装了[ffmpeg](https://ffmpeg.org/ffmpeg.html), Linux(Ubuntu)平台可以直接用以下命令安装：`apt-get update && apt-get install -y ffmpeg`。
+
 ## 引用
 ```
 @inproceedings{Wojke2017simple,

--- a/configs/mot/jde/README.md
+++ b/configs/mot/jde/README.md
@@ -18,7 +18,7 @@ English | [简体中文](README_cn.md)

 ## Model Zoo

-### JDE on MOT-16 Training Set
+### JDE Results on MOT-16 Training Set

 | backbone           | input shape | MOTA | IDF1  |  IDS  |   FP  |  FN  |  FPS  | download | config |
 | :----------------- | :------- | :----: | :----: | :---: | :----: | :---: | :---: | :---: | :---: |
@@ -26,7 +26,7 @@ English | [简体中文](README_cn.md)
 | DarkNet53          | 864x480 |  69.1  |  64.7  | 1539  |  7544  | 25046 |   -   |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_864x480.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_864x480.yml) |
 | DarkNet53          | 576x320 |  63.7  |  64.4  | 1310  |  6782  | 31964 |   -   |[model](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_576x320.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/jde/jde_darknet53_30e_576x320.yml) |

-### JDE on MOT-16 Test Set
+### JDE Results on MOT-16 Test Set

 | backbone           | input shape | MOTA | IDF1  |  IDS  |   FP  |  FN  |  FPS  | download | config |
 | :----------------- | :------- | :----: | :----: | :---: | :----: | :---: | :---: | :---: | :---: |

--- a/ppdet/modeling/mot/utils.py
+++ b/ppdet/modeling/mot/utils.py
@@ -113,8 +113,8 @@ def load_det_results(det_file, num_frames):
        results = {'bbox': [], 'score': []}
        lables_with_frame = labels[labels[:, 0] == frame_i + 1]
        for l in lables_with_frame:
-            results['bbox'].append(l[2:6])
-            results['score'].append(l[6])
+            results['bbox'].append(l[1:5])
+            results['score'].append(l[5])
        results_list.append(results)
    return results_list