SORT的readme

SORT
=====

A simple online and realtime tracking algorithm for 2D multiple object tracking in video sequences.
See an example [video here]   (https://motchallenge.net/movies/ETH-Linthescher-SORT.mp4).
例子视频

By Alex Bewley  

### Introduction

SORT is a barebones implementation of a visual multiple object tracking framework based on rudimentary data association and state estimation techniques. It is designed for online tracking applications where only past and current frames are available and the method produces object identities on the fly. While this minimalistic tracker doesn't handle occlusion or re-entering objects its purpose is to serve as a baseline and testbed for the development of future trackers.

SORT was initially described in an [arXiv tech report](http://arxiv.org/abs/1602.00763). At the time of the initial publication, SORT was ranked the best *open source* multiple object tracker on the [MOT benchmark](https://motchallenge.net/results/2D_MOT_2015/).

This code has been tested on Mac OSX 10.10, and Ubuntu 14.04, with Python 2.7 (anaconda).

**Note:** A significant proportion of SORT's accuracy is attributed to the detections.
For your convenience, this repo also contains *Faster* RCNN detections for the MOT benchmark sequences in the [benchmark format](https://motchallenge.net/instructions/).
To run the detector yourself please see the original [*Faster* RCNN project]   任少卿的检测器 (https://github.com/ShaoqingRen/faster_rcnn)
or the python 版本reimplementation of [py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn) by Ross Girshick.

**Also see:**
A new and improved version of SORT with a Deep Association Metric implemented in tensorflow is available at [https://github.com/nwojke/deep_sort](https://github.com/nwojke/deep_sort) .

### License

SORT is released under the GPL License (refer to the LICENSE file for details) to promote the open use of the tracker and future improvements. If you require a permissive license contact Alex (alex@bewley.ai).

引用而已

### Citing SORT

If you find this repo useful in your research, please consider citing:

    @inproceedings{Bewley2016_sort,
      author={Bewley, Alex and Ge, Zongyuan and Ott, Lionel and Ramos, Fabio and Upcroft, Ben},
      booktitle={2016 IEEE International Conference on Image Processing (ICIP)},
      title={Simple online and realtime tracking},
      year={2016},
      pages={3464-3468},
      keywords={Benchmark testing;Complexity theory;Detectors;Kalman filters;Target tracking;Visualization;Computer Vision;Data Association;Detection;Multiple Object Tracking},
      doi={10.1109/ICIP.2016.7533003}
    }

### Dependencies:依赖

This code makes use of the following packages:
1. [`scikit-learn`](　　http://scikit-learn.org/stable/　)有了
0. [`scikit-image`](　　http://scikit-image.org/download　)
0. [`FilterPy`](　　https://github.com/rlabbe/filterpy　)有了

To install required dependencies run:
```
$ pip install -r requirements.txt
```

### Demo:

To run the tracker with the provided detections:

```
$ cd path/to/sort
$ python sort.py
```

To display the results you need to:

0. Download the [2D MOT 2015 benchmark dataset](　https://motchallenge.net/data/2D_MOT_2015/#download　)
0. Create a symbolic link to the dataset　　符号链接
  ```
  $ ln -s /path/to/MOT2015_challenge/data/2DMOT2015 mot_benchmark

换为路径
  ```
0. Run the demo with the ```--display``` flag
  ```
  $ python sort.py --display
  ```

### Main Results

Using the [MOT challenge devkit]　　(　https://motchallenge.net/devkit/　) the method produces the following results (as described in the paper).

 Sequence       | Rcll | Prcn |  FAR | GT  MT  PT  ML|   FP    FN  IDs   FM|  MOTA  MOTP MOTAL
--------------- |:----:|:----:|:----:|:-------------:|:-------------------:|:------------------:
 TUD-Campus     | 68.5 | 94.3 | 0.21 |  8   6   2   0|   15   113    6    9|  62.7  73.7  64.1
 ETH-Sunnyday   | 77.5 | 81.9 | 0.90 | 30  11  16   3|  319   418   22   54|  59.1  74.4  60.3
 ETH-Pedcross2  | 51.9 | 90.8 | 0.39 | 133  17  60  56|  330  3014   77  103|  45.4  74.8  46.6
 ADL-Rundle-8   | 44.3 | 75.8 | 1.47 | 28   6  16   6|  959  3781  103  211|  28.6  71.1  30.1
 Venice-2       | 42.5 | 64.8 | 2.75 | 26   7   9  10| 1650  4109   57  106|  18.6  73.4  19.3
 KITTI-17       | 67.1 | 92.3 | 0.26 |  9   1   8   0|   38   225    9   16|  60.2  72.3  61.3
 *Overall*      | 49.5 | 77.5 | 1.24 | 234  48 111  75| 3311 11660  274  499|  34.0  73.3  35.1

### Using SORT in your own project

Below is the gist of how to instantiate and update SORT.实例化和更新的要点

See the ['__main__']　(　https://github.com/abewley/sort/blob/master/sort.py#L239　) 　ection of [sort.py](　https://github.com/abewley/sort/blob/master/sort.py#L239　) for a complete example.
    
    from sort import *
    
    #create instance of SORT
    mot_tracker = Sort()
    
    # get detections
    ...
    
    # update SORT
    track_bbs_ids = mot_tracker.update(detections)

    # track_bbs_ids 　　is a np array 　　　where each row 　　contains a valid bounding box 　and track_id (last column)
    ...
    
 

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