sort跟踪算法代码包

SORT ===== A simple online and realtime tracking algorithm for 2D multiple object tracking in video sequences. See an example [video here](https://alex.bewley.ai/misc/SORT-MOT17-06-FRCNN.webm). 一种简单的在线实时跟踪算法，用于视频序列中的二维多目标跟踪。 参见示例[此处视频](https://alex.bewley.ai/misc/SORT-MOT17-06-FRCNN.webm). 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是基于基本数据关联和状态估计技术的可视多目标跟踪框架的基本实现。它是为在线跟踪应用而设计的，在这些应用中，只有过去和当前帧可用，并且该方法可以动态生成对象身份。虽然这种极简跟踪器不处理遮挡或重新进入的物体，但它的目的是作为未来跟踪器开发的基准和测试平台。 SORT was initially described in [this paper](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/). SORT最初是在[本文]中描述的(http://arxiv.org/abs/1602.00763).在首次发布时，SORT被评为[MOT基准测试]上最好的*开源*多对象跟踪器(https://motchallenge.net/results/2D_MOT_2015/). **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](https://github.com/ShaoqingRen/faster_rcnn) or the python 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 ([email protected]). ### 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: #To install required dependencies run: #要安装所需的依赖项，请运行： ``` $ pip install -r requirements.txt ``` pip install -r requirements.txt -i https://pypi.mirrors.ustc.edu.cn/simple/ --default-timeout=5000 ### Demo:演示： #要使用提供的检测运行跟踪器： To run the tracker with the provided detections: ``` $ cd path/to/sort $ python sort.py ``` To display the results you need to: 要显示结果，您需要： 1. Download the [2D MOT 2015 benchmark dataset](https://motchallenge.net/data/2D_MOT_2015/#download) 1、下载[2D MOT 2015基准数据集] 2. Create a symbolic link to the dataset 2、.创建指向数据集的符号链接 ``` $ ln -s /path/to/MOT2015_challenge/data/2DMOT2015 mot_benchmark\ mklink /d "E:\PycharmProjects\pythonProject2\sort-master\datasets\2DMOT2015" "E:/PycharmProjects/pythonProject2/datasets/2DMOT2015" ln -s /archive/gyj/sort-master/datasets/MOT2015_challenge/data/2DMOT2015 /archive/gyj/datasets/2DMOT2015 ln -s /archive/gyj/sort-master/data/2DMOT2015 mot_benchmark\ 0. Run the demo with the ```--display``` flag 3、.使用“`--display``标志运行演示 ``` $ python sort.py --display ``` ### Main Results 结果 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 在自己的项目中使用 Below is the gist of how to instantiate and update SORT. See the ['__main__'](https://github.com/abewley/sort/blob/master/sort.py#L239) section of [sort.py](https://github.com/abewley/sort/blob/master/sort.py#L239) for a complete example. 下面是如何实例化和更新排序的要点。请参见 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) ...