基于transformer的视频对象识别跟踪.zip

# PyTracking A general python library for visual tracking algorithms. ## Table of Contents * [Running a tracker using Pytracking toolkit](#running-a-tracker-using-pytracking-toolkit) * [Running a tracker using GOT-10k toolkit](#running-a-tracker-using-got-10k-toolkit) * [Overview](#overview) * [Trackers](#trackers) * [TrDiMP and TrSiam](#trdimp-and-trsiam) * [ATOM](#ATOM) * [ECO](#ECO) * [Analysis](#analysis) * [Libs](#libs) * [Integrating a new tracker](#integrating-a-new-tracker) ## Running a tracker using PyTracking Toolkit The installation script will automatically generate a local configuration file "evaluation/local.py". In case the file was not generated, run ```evaluation.environment.create_default_local_file()``` to generate it. Next, set the paths to the datasets you want to use for evaluations. You can also change the path to the networks folder, and the path to the results folder, if you do not want to use the default paths. If all the dependencies have been correctly installed, you are set to run the trackers. The toolkit provides many ways to run a tracker. **Run the tracker on webcam feed** This is done using the run_webcam script. The arguments are the name of the tracker, and the name of the parameter file. You can select the object to track by drawing a bounding box. **Note:** It is possible to select multiple targets to track! ```bash python run_webcam.py tracker_name parameter_name ``` **Run the tracker on some dataset sequence** This is done using the run_tracker script. ```bash python run_tracker.py tracker_name parameter_name --dataset_name dataset_name --sequence sequence --debug debug --threads threads ``` Here, the dataset_name is the name of the dataset used for evaluation, e.g. ```otb```. See [evaluation.datasets.py](evaluation/datasets.py) for the list of datasets which are supported. The sequence can either be an integer denoting the index of the sequence in the dataset, or the name of the sequence, e.g. ```'Soccer'```. The ```debug``` parameter can be used to control the level of debug visualizations. ```threads``` parameter can be used to run on multiple threads. **Run the tracker on a set of datasets** This is done using the run_experiment script. To use this, first you need to create an experiment setting file in ```pytracking/experiments```. See [myexperiments.py](experiments/myexperiments.py) for reference. ```bash python run_experiment.py experiment_module experiment_name --dataset_name dataset_name --sequence sequence --debug debug --threads threads ``` Here, ```experiment_module``` is the name of the experiment setting file, e.g. ```myexperiments``` , and ``` experiment_name``` is the name of the experiment setting, e.g. ``` atom_nfs_uav``` . Examples: run TrDiMP/TrSiam on the TrackingNet: ```bash python run_experiment.py myexperiments trdimp_trackingnet ``` ```bash python run_experiment.py myexperiments trsiam_trackingnet ``` **Run the tracker on a video file** This is done using the run_video script. ```bash python run_video.py experiment_module experiment_name videofile --optional_box optional_box --debug debug ``` Here, ```videofile``` is the path to the video file. You can either draw the box by hand or provide it directly in the ```optional_box``` argument. ## Running a tracker using GOT-10k Toolkit To evaluate the tracker on the GOT-10k benchmark, you can download the GOT-10k toolkit using ```pip```: ```bash pip install --upgrade got10k ``` For more details, please refer to GOT-10K ([Github](https://github.com/got-10k/toolkit.git)). To run and evaluate the tracker using GOT-10k toolkit, you have to modify the ```/tracker/trdimp/trdimp.py``` to ensure it supports the input and output formats of GOT-10k toolkit. ```/tracker/trdimp/trdimp_for_GOT.py``` is an example. **Run the tracker on the GOT-10k** This is done using the provided ```GOT10k_GOT.py``` script. You can also write your own script. More details can be found in [GOT-10k](https://github.com/got-10k/toolkit.git) ```bash python GOT10k_GOT.py --tracker_name tracker_name --tracker_param tracker_param ``` Here, ```tracker_name``` is the name of tracker, e.g. ```trdimp```. ```tracker_param``` is the parameter setting, e.g. ```trdimp``` and ```trsiam```. **Run the tracker on other benchmarks using GOT-10k toolkit** Please refer to ```GOT10k_NFS.py```, ```GOT10k_UAV.py```, ```GOT10k_VOT.py``` for detials. Do not forget to change the dataset path in these scripts. For example, to run and evaluate the TrDiMP and TrSiam on the NFS dataset: ```bash python GOT10k_NFS.py --tracker_name trdimp --tracker_param trdimp ``` ```bash python GOT10k_NFS.py --tracker_name trdimp --tracker_param trsiam ``` ## Overview The tookit consists of the following sub-modules. - [analysis](analysis): Contains scripts to analyse tracking performance, e.g. obtain success plots, compute AUC score. It also contains a [script](analysis/playback_results.py) to playback saved results for debugging. - [evaluation](evaluation): Contains the necessary scripts for running a tracker on a dataset. It also contains integration of a number of standard tracking and video object segmentation datasets, namely [OTB-100](http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html), [NFS](http://ci2cv.net/nfs/index.html), [UAV123](https://ivul.kaust.edu.sa/Pages/pub-benchmark-simulator-uav.aspx), [Temple128](http://www.dabi.temple.edu/~hbling/data/TColor-128/TColor-128.html), [TrackingNet](https://tracking-net.org/), [GOT-10k](http://got-10k.aitestunion.com/), [LaSOT](https://cis.temple.edu/lasot/), [VOT](http://www.votchallenge.net), [Temple Color 128](http://www.dabi.temple.edu/~hbling/data/TColor-128/TColor-128.html), [DAVIS](https://davischallenge.org), and [YouTube-VOS](https://youtube-vos.org). - [experiments](experiments): The experiment setting files must be stored here, - [features](features): Contains tools for feature extraction, data augmentation and wrapping networks. - [libs](libs): Includes libraries for optimization, dcf, etc. - [notebooks](notebooks) Jupyter notebooks to analyze tracker performance. - [parameter](parameter): Contains the parameter settings for different trackers. - [tracker](tracker): Contains the implementations of different trackers. - [util_scripts](util_scripts): Some util scripts for e.g. generating packed results for evaluation on GOT-10k and TrackingNet evaluation servers, downloading pre-computed results. - [utils](utils): Some util functions. - [VOT](VOT): VOT Integration. ## Trackers The toolkit contains the implementation of the following trackers. ### TrDiMP and TrSiam The official implementation for the TrDiMP tracker and TrSiam tracker. The tracker implementation file can be found at [tracker.trdimp](tracker/trdimp). ##### Parameter Files Illustrations of the parameter settings. * **[trdimp](parameter/trdimp/trdimp.py)**: The default parameter setting with ResNet-50 backbone which was used to produce TrDiMP results in the paper, except on VOT and LaSOT. * **[trsiam](parameter/trdimp/trsiam.py)**: The default parameter setting with ResNet-50 backbone which was used to produce TrSiam results in the paper, except on VOT and LaSOT. * **[trdimp_vot](parameter/trdimp/trdimp_vot.py)**: The parameters settings used to generate the TrDiMP VOT2018 results in the paper. * **[trdimp_lasot](parameter/trdimp/trdimp_lasot.py)**: The parameters settings used to generate the TrDiMP LaSOT results in the paper. The difference between the VOT and the non-VOT settings stems from the fact that the VOT protocol measures robustness in a very different manner compared to other benchmarks. In most benchmarks, it is highly important to be able to robustly *redetect* the target after e.g. an occlusion or brief target loss. On the other hand, in VOT the tracker is reset if the prediction does not overlap with the target on a *single* frame. This is then counted as a