# TCTrack

**Repository Path**: vt-developer/TCTrack

## Basic Information

- **Project Name**: TCTrack
- **Description**: No description available
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-12-09
- **Last Updated**: 2023-12-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TCTrack: Temporal Contexts for Aerial Tracking （CVPR2022) & TCTrack++：Towards Real-World Visual Tracking with Temporal Contexts （TPAMI）

[Ziang Cao](https://ziangcao0312.github.io/) and [Ziyuan Huang](https://huang-ziyuan.github.io/) and [Liang Pan](https://scholar.google.com/citations?user=lSDISOcAAAAJ&hl=zh-CN&authuser=1) and Shiwei Zhang and [Ziwei Liu](https://liuziwei7.github.io/) and [Changhong Fu](https://vision4robotics.github.io/authors/changhong-fu/)

In CVPR, 2022.

[[paper](https://arxiv.org/abs/2203.01885)] [[paper_journal](https://arxiv.org/abs/2308.10330)]

## Abstract
Temporal contexts among consecutive frames are far
from being fully utilized in existing visual trackers. In this work, we present TCTrack, a comprehensive framework to fully exploit temporal contexts for aerial tracking. The temporal contexts are incorporated at two levels: the extraction of features and the refinement of similarity maps. Specifically, for feature extraction, an online temporally adaptive convolution is proposed to enhance the spatial features using temporal information, which is achieved by dynamically calibrating the convolution weights according to the previous frames. For similarity map refinement, we propose an adaptive temporal transformer, which first effectively encodes
temporal knowledge in a memory-efficient way, before
the temporal knowledge is decoded for accurate adjustment
of the similarity map. TCTrack is effective and efficient:
evaluation on four aerial tracking benchmarks shows
its impressive performance; real-world UAV tests show its
high speed of over 27 FPS on NVIDIA Jetson AGX Xavier.

![Workflow of our tracker](https://github.com/vision4robotics/TCTrack/blob/main/images/workflow.jpg)


The implementation of our online temporally adaptive convolution is based on [TadaConv](https://github.com/alibaba-mmai-research/TAdaConv) (ICLR2022).


## 1. Environment setup
This code has been tested on Ubuntu 18.04, Python 3.8.3, Pytorch 0.7.0/1.6.0, CUDA 10.2.
Please install related libraries before running this code: 
```bash
pip install -r requirements.txt
```

## 2. Test

### (a) TCTrack
Download pretrained model by [Baidu](https://pan.baidu.com/s/1jSAcHY9OfarVlxKjOCrVEw) （code: 2u1l) or [Googledrive](https://drive.google.com/file/d/1nWRfvAEcSduR9A4W5MpyZBjp0SCjvmNk/view?usp=sharing) and put it into `tools/snapshot` directory.

Download testing datasets and put them into `test_dataset` directory. 

```bash 
python ./tools/test.py                                
	--dataset OTB100                  
    --tracker_name TCTrack
	--snapshot snapshot/general_model.pth # pre-train model path
```
The testing result will be saved in the `results/dataset_name/tracker_name` directory.

**Note:** The results of TCTrack can be [downloaded](https://pan.baidu.com/s/1-V4JbKvmVPm0aOKWTOQtyQ) (code:kh3e).

### (b) TCTrack++
Download pretrained model by [baidu](https://pan.baidu.com/s/1aggubJ4F-YdMtEo7t0lYtw?pwd=dj2u) (code:dj2u) [Googledrive](https://drive.google.com/file/d/1yHLZTPkU_Mko0OX03fd2HH01g0gflusI/view?usp=sharing) and put it into `tools/snapshot` directory.

Download testing datasets and put them into `test_dataset` directory. 

```bash 
python ./tools/test.py     # offline evaluation                       
	--dataset OTB100                  
    --tracker_name TCTrack++
	--snapshot snapshot/general_model.pth # pre-train model path

```
```bash 
python ./tools/test_rt.py     # online evaluation                       
	--dataset OTB100                  
    --tracker_name TCTrack++
	--snapshot snapshot/general_model.pth # pre-train model path
```

The testing result will be saved in the `results/dataset_name/tracker_name` directory.

**Note:** The results of TCTrack++ can be [downloaded](https://drive.google.com/file/d/1TaolHsyOy_zIkm-MEEkMZuOtbr_NuUYC/view?usp=sharing) or [downloaded](https://pan.baidu.com/s/1v7ie10TmFDiWKoosTESXTw?pwd=3vyx) (code: 3vyx).

## 3. Train

### (a) TCTrack

#### Prepare training datasets

Download the datasets：
* [VID](http://image-net.org/challenges/LSVRC/2017/)
* [Lasot](https://paperswithcode.com/dataset/lasot)
* [GOT-10K](http://got-10k.aitestunion.com/downloads)


**Note:** `train_dataset/dataset_name/readme.md` has listed detailed operations about how to generate training datasets.

#### Train a model
To train the TCTrack and TCTrack++ model, run `train.py` with the desired configs:

```bash
cd tools
python train_tctrack.py
```

### (b) TCTrack++

#### Prepare training datasets

Download the datasets：
* [VID](http://image-net.org/challenges/LSVRC/2017/)
* [Lasot](https://paperswithcode.com/dataset/lasot)
* [GOT-10K](http://got-10k.aitestunion.com/downloads)
* [COCO](http://cocodataset.org)

**Note:** `train_dataset/dataset_name/readme.md` has listed detailed operations about how to generate training datasets.

### Train a model
To train the TCTrack and TCTrack++ model, run `train.py` with the desired configs:

```bash
cd tools
python train_tctrackpp.py
```

## 4. Offline Evaluation
If you want to evaluate the results of our tracker, please put those results into  `results` directory.
```
python eval.py 	                          \
	--tracker_path ./results          \ # result path
	--dataset OTB100                  \ # dataset_name
	--tracker_prefix 'general_model'   # tracker_name
```

## 5. Online Evaluation
If you want to evaluate the results of our tracker, please put the pkl files into  `results_rt_raw` directory.


```
#first step

python rt_eva.py 	                          \
	--raw_root ./tools/results_rt_raw/OTB100          \ # pkl path
	--tar_root ./tools/results_rt/OTB100                  \ # output txt files for evaluation
	--gtroot ./test_dataset/OTB100   # groundtruth of dataset
```

```
# second step
python eval.py 	                          \
	--tracker_path ./results_rt          \ # result path
	--dataset OTB100                  \ # dataset_name
	--trackers TCTrack++   # tracker_name
```


**Note:** The code is implemented based on [pysot-toolkit](https://github.com/StrangerZhang/pysot-toolkit). We would like to express our sincere thanks to the contributors.

## Demo video
[![TCTrack](https://res.cloudinary.com/marcomontalbano/image/upload/v1646040242/video_to_markdown/images/youtube--wcR3iCFJN4E-c05b58ac6eb4c4700831b2b3070cd403.jpg)](https://youtu.be/wcR3iCFJN4E "TCTrack")

## References 

```
@inproceedings{cao2022tctrack,
	title={{TCTrack: Temporal Contexts for Aerial Tracking}},
	author={Cao, Ziang and Huang, Ziyuan and Pan, Liang and Zhang, Shiwei and Liu, Ziwei and Fu, Changhong},
	booktitle={CVPR},
	pages={14798--14808},
	year={2022}
}

@article{cao2023towards,
  title={Towards Real-World Visual Tracking with Temporal Contexts},
  author={Cao, Ziang and Huang, Ziyuan and Pan, Liang and Zhang, Shiwei and Liu, Ziwei and Fu, Changhong},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2023},
  publisher={IEEE}
}

```

## Acknowledgement
The code is implemented based on [pysot](https://github.com/STVIR/pysot). We would like to express our sincere thanks to the contributors.