The current mainstream multi-objective tracking (MOT) algorithm is mainly composed of two parts: detection and embedding. Detection aims to detect the potential targets in each frame of the video. Embedding assigns and updates the detected target to the corresponding track (named ReID task). According to the different implementation of these two parts, it can be divided into **SDE** series and **JDE** series algorithm.
-**SDE** (Separate Detection and Embedding) is a kind of algorithm which completely separates Detection and Embedding. The most representative is **DeepSORT** algorithm. This design can make the system fit any kind of detectors without difference, and can be improved for each part separately. However, due to the series process, the speed is slow. Time-consuming is a great challenge in the construction of real-time MOT system.
-**JDE** (Joint Detection and Embedding) is to learn detection and embedding simultaneously in a shared neural network, and set the loss function with a multi task learning approach. The representative algorithms are **JDE** and **FairMOT**. This design can achieve high-precision real-time MOT performance.
-SDE(Separate Detection and Embedding)这类算法完全分离Detection和Embedding两个环节，最具代表性的就是**DeepSORT**算法。这样的设计可以使系统无差别的适配各类检测器，可以针对两个部分分别调优，但由于流程上是串联的导致速度慢耗时较长，在构建实时MOT系统中面临较大挑战。
Paddledetection implements three MOT algorithms of these two series.
- JDE(Joint Detection and Embedding)这类算法完是在一个共享神经网络中同时学习Detection和Embedding，使用一个多任务学习的思路设置损失函数。代表性的算法有**JDE**和**FairMOT**。这样的设计兼顾精度和速度，可以实现高精度的实时多目标跟踪。
-[DeepSORT](https://arxiv.org/abs/1812.00442)(Deep Cosine Metric Learning SORT) extends the original [SORT](https://arxiv.org/abs/1703.07402)(Simple Online and Realtime Tracking) algorithm, it adds a CNN model to extract features in image of human part bounded by a detector. It integrates appearance information based on a deep appearance descriptor, and assigns and updates the detected targets to the existing corresponding trajectories like ReID task. The detection bboxes result required by DeepSORT can be generated by any detection model, and then the saved detection result file can be loaded for tracking. Here we select the `PCB + Pyramid ResNet101` and `PPLCNet` models provided by [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) as the ReID model.
-[JDE](https://arxiv.org/abs/1909.12605)(Joint Detection and Embedding) learns the object detection task and appearance embedding task simutaneously in a shared neural network. And the detection results and the corresponding embeddings are also outputed at the same time. JDE original paper is based on an Anchor Base detector YOLOv3 , adding a new ReID branch to learn embeddings. The training process is constructed as a multi-task learning problem, taking into account both accuracy and speed.
-[JDE](https://arxiv.org/abs/1909.12605)(Joint Detection and Embedding)是在一个单一的共享神经网络中同时学习目标检测任务和embedding任务，并同时输出检测结果和对应的外观embedding匹配的算法。JDE原论文是基于Anchor Base的YOLOv3检测器新增加一个ReID分支学习embedding，训练过程被构建为一个多任务联合学习问题，兼顾精度和速度。
-[FairMOT](https://arxiv.org/abs/2004.01888) is based on an Anchor Free detector Centernet, which overcomes the problem of anchor and feature misalignment in anchor based detection framework. The fusion of deep and shallow features enables the detection and ReID tasks to obtain the required features respectively. It also uses low dimensional ReID features. FairMOT is a simple baseline composed of two homogeneous branches propose to predict the pixel level target score and ReID features. It achieves the fairness between the two tasks and obtains a higher level of real-time MOT performance.
[PP-Tracking](../../deploy/pptracking/README.md) is the first open source real-time tracking system based on PaddlePaddle deep learning framework. Aiming at the difficulties and pain points of the actual business, PP-Tracking has built-in capabilities and industrial applications such as pedestrian and vehicle tracking, cross-camera tracking, multi-class tracking, small target tracking and traffic counting, and provides a visual development interface. The model integrates multi-object tracking, object detection and ReID lightweight algorithm to further improve the deployment performance of PP-Tracking on the server. It also supports Python and C + + deployment and adapts to Linux, NVIDIA and Jetson multi platform environment.。
@@ -32,46 +32,44 @@ Paddledetection implements three MOT algorithms of these two series.
...
@@ -32,46 +32,44 @@ Paddledetection implements three MOT algorithms of these two series.
<divwidth="1000"align="center">
<divwidth="1000"align="center">
<imgsrc="../../docs/images/pptracking-demo.gif"/>
<imgsrc="../../docs/images/pptracking-demo.gif"/>
<br>
<br>
video source：VisDrone2021, BDD100K dataset</div>
视频来源：VisDrone2021, BDD100K开源数据集</div>
</div>
</div>
## Installation
## 安装依赖
Install all the related dependencies for MOT:
一键安装MOT相关的依赖：
```
```
pip install lap sklearn motmetrics openpyxl cython_bbox
pip install lap sklearn motmetrics openpyxl cython_bbox
or
或者
pip install -r requirements.txt
pip install -r requirements.txt
```
```
**Notes:**
**注意：**
- Install `cython_bbox` for Windows: `pip install -e git+https://github.com/samson-wang/cython_bbox.git#egg=cython-bbox`. You can refer to this [tutorial](https://stackoverflow.com/questions/60349980/is-there-a-way-to-install-cython-bbox-for-windows).
- 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`.
PaddleDetection use the same training data as [JDE](https://github.com/Zhongdao/Towards-Realtime-MOT) and [FairMOT](https://github.com/ifzhang/FairMOT). Please refer to [PrepareMOTDataSet](../../docs/tutorials/PrepareMOTDataSet.md) to download and prepare all the training data including **Caltech Pedestrian, CityPersons, CUHK-SYSU, PRW, ETHZ, MOT17 and MOT16**. The former six are used as the mixed dataset for training, and MOT16 are used as the evaluation dataset. In addition, you can use **MOT15 and MOT20** for finetune. All pedestrians in these datasets have detection bbox labels and some have ID labels. If you want to use these datasets, please **follow their licenses**.
### 数据格式
这几个相关数据集都遵循以下结构：
### Data Format
These several relevant datasets have the following structure:
```
```
Caltech
Caltech
|——————images
|——————images
...
@@ -89,24 +87,22 @@ MOT17
...
@@ -89,24 +87,22 @@ MOT17
└——————labels_with_ids
└——————labels_with_ids
└——————train
└——————train
```
```
Annotations of these datasets are provided in a unified format. Every image has a corresponding annotation text. Given an image path, the annotation text path can be generated by replacing the string `images` with `labels_with_ids` and replacing `.jpg` with `.txt`.
-`class` should be `0`. Only single-class multi-object tracking is supported now.
-`class`为`0`，目前仅支持单类别多目标跟踪。
-`identity` is an integer from `1` to `num_identities`(`num_identities` is the total number of instances of objects in the dataset), or `-1` if this box has no identity annotation.
-`[x_center] [y_center] [width] [height]` are the center coordinates, width and height, note that they are normalized by the width/height of the image, so they are floating point numbers ranging from 0 to 1.
The current mainstream multi-objective tracking (MOT) algorithm is mainly composed of two parts: detection and embedding. Detection aims to detect the potential targets in each frame of the video. Embedding assigns and updates the detected target to the corresponding track (named ReID task). According to the different implementation of these two parts, it can be divided into **SDE** series and **JDE** series algorithm.
-**SDE** (Separate Detection and Embedding) is a kind of algorithm which completely separates Detection and Embedding. The most representative is **DeepSORT** algorithm. This design can make the system fit any kind of detectors without difference, and can be improved for each part separately. However, due to the series process, the speed is slow. Time-consuming is a great challenge in the construction of real-time MOT system.
-SDE(Separate Detection and Embedding)这类算法完全分离Detection和Embedding两个环节，最具代表性的就是**DeepSORT**算法。这样的设计可以使系统无差别的适配各类检测器，可以针对两个部分分别调优，但由于流程上是串联的导致速度慢耗时较长，在构建实时MOT系统中面临较大挑战。
-**JDE** (Joint Detection and Embedding) is to learn detection and embedding simultaneously in a shared neural network, and set the loss function with a multi task learning approach. The representative algorithms are **JDE** and **FairMOT**. This design can achieve high-precision real-time MOT performance.
- JDE(Joint Detection and Embedding)这类算法完是在一个共享神经网络中同时学习Detection和Embedding，使用一个多任务学习的思路设置损失函数。代表性的算法有**JDE**和**FairMOT**。这样的设计兼顾精度和速度，可以实现高精度的实时多目标跟踪。
Paddledetection implements three MOT algorithms of these two series.
PaddleDetection实现了这两个系列的3种多目标跟踪算法。
-[DeepSORT](https://arxiv.org/abs/1812.00442)(Deep Cosine Metric Learning SORT) extends the original [SORT](https://arxiv.org/abs/1703.07402)(Simple Online and Realtime Tracking) algorithm, it adds a CNN model to extract features in image of human part bounded by a detector. It integrates appearance information based on a deep appearance descriptor, and assigns and updates the detected targets to the existing corresponding trajectories like ReID task. The detection bboxes result required by DeepSORT can be generated by any detection model, and then the saved detection result file can be loaded for tracking. Here we select the `PCB + Pyramid ResNet101` and `PPLCNet` models provided by [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) as the ReID model.
-[JDE](https://arxiv.org/abs/1909.12605)(Joint Detection and Embedding)是在一个单一的共享神经网络中同时学习目标检测任务和embedding任务，并同时输出检测结果和对应的外观embedding匹配的算法。JDE原论文是基于Anchor Base的YOLOv3检测器新增加一个ReID分支学习embedding，训练过程被构建为一个多任务联合学习问题，兼顾精度和速度。
-[JDE](https://arxiv.org/abs/1909.12605)(Joint Detection and Embedding) learns the object detection task and appearance embedding task simutaneously in a shared neural network. And the detection results and the corresponding embeddings are also outputed at the same time. JDE original paper is based on an Anchor Base detector YOLOv3 , adding a new ReID branch to learn embeddings. The training process is constructed as a multi-task learning problem, taking into account both accuracy and speed.
-[FairMOT](https://arxiv.org/abs/2004.01888) is based on an Anchor Free detector Centernet, which overcomes the problem of anchor and feature misalignment in anchor based detection framework. The fusion of deep and shallow features enables the detection and ReID tasks to obtain the required features respectively. It also uses low dimensional ReID features. FairMOT is a simple baseline composed of two homogeneous branches propose to predict the pixel level target score and ReID features. It achieves the fairness between the two tasks and obtains a higher level of real-time MOT performance.
[PP-Tracking](../../deploy/pptracking/README.md) is the first open source real-time tracking system based on PaddlePaddle deep learning framework. Aiming at the difficulties and pain points of the actual business, PP-Tracking has built-in capabilities and industrial applications such as pedestrian and vehicle tracking, cross-camera tracking, multi-class tracking, small target tracking and traffic counting, and provides a visual development interface. The model integrates multi-object tracking, object detection and ReID lightweight algorithm to further improve the deployment performance of PP-Tracking on the server. It also supports Python and C + + deployment and adapts to Linux, NVIDIA and Jetson multi platform environment.。
- Install `cython_bbox` for Windows: `pip install -e git+https://github.com/samson-wang/cython_bbox.git#egg=cython-bbox`. You can refer to this [tutorial](https://stackoverflow.com/questions/60349980/is-there-a-way-to-install-cython-bbox-for-windows).
- 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`.
PaddleDetection use the same training data as [JDE](https://github.com/Zhongdao/Towards-Realtime-MOT) and [FairMOT](https://github.com/ifzhang/FairMOT). Please refer to [PrepareMOTDataSet](../../docs/tutorials/PrepareMOTDataSet.md) to download and prepare all the training data including **Caltech Pedestrian, CityPersons, CUHK-SYSU, PRW, ETHZ, MOT17 and MOT16**. The former six are used as the mixed dataset for training, and MOT16 are used as the evaluation dataset. In addition, you can use **MOT15 and MOT20** for finetune. All pedestrians in these datasets have detection bbox labels and some have ID labels. If you want to use these datasets, please **follow their licenses**.
这几个相关数据集都遵循以下结构：
### Data Format
These several relevant datasets have the following structure:
Annotations of these datasets are provided in a unified format. Every image has a corresponding annotation text. Given an image path, the annotation text path can be generated by replacing the string `images` with `labels_with_ids` and replacing `.jpg` with `.txt`.
In the annotation text, each line is describing a bounding box and has the following format:
-`identity` is an integer from `1` to `num_identities`(`num_identities` is the total number of instances of objects in the dataset), or `-1` if this box has no identity annotation.
-`[x_center] [y_center] [width] [height]` are the center coordinates, width and height, note that they are normalized by the width/height of the image, so they are floating point numbers ranging from 0 to 1.
### 数据集目录
### Dataset Directory
首先按照以下命令下载image_lists.zip并解压放在`dataset/mot`目录下：
First, follow the command below to download the `image_list.zip` and unzip it in the `dataset/mot` directory:
Then download and unzip each dataset, and the final directory is as follows:
```
```
dataset/mot
dataset/mot
|——————image_lists
|——————image_lists
...
@@ -132,8 +136,20 @@ dataset/mot
...
@@ -132,8 +136,20 @@ dataset/mot
|——————PRW
|——————PRW
```
```
### Quick Download
You can download all the dataset according to the following command. Note that it needs to be decompressed and stored according to the above directory.
python deploy/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --save_mot_txts
python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --save_mot_txts
```
```
**Notes:**
**Notes:**
The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_mot_txts` to save the txt result file, or `--save_images` to save the visualization images.
The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_mot_txts` to save the txt result file, or `--save_images` to save the visualization images.
python deploy/python/mot_jde_infer.py --model_dir=output_inference/jde_darknet53_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --save_mot_txts
python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/jde_darknet53_30e_1088x608 --video_file={your video name}.mp4 --device=GPU --save_mot_txts
```
```
**Notes:**
**Notes:**
The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_mot_txts` to save the txt result file, or `--save_images` to save the visualization images.
The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_mot_txts` to save the txt result file, or `--save_images` to save the visualization images.
python deploy/python/mot_jde_infer.py --model_dir=output_inference/mcfairmot_dla34_30e_1088x608_visdrone --video_file={your video name}.mp4 --device=GPU --save_mot_txts
python deploy/pptracking/python/mot_jde_infer.py --model_dir=output_inference/mcfairmot_dla34_30e_1088x608_visdrone --video_file={your video name}.mp4 --device=GPU --save_mot_txts
```
```
**Notes:**
**Notes:**
The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_mot_txts` to save the txt result file, or `--save_images` to save the visualization images.
The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_mot_txts` to save the txt result file, or `--save_images` to save the visualization images.
