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DiffusionTrack: Point set Diffussion Model for Visual Object Tracking

Fei Xie, Zhongdao Wang, Chao Ma

:star: The official implementation for the CVPR2024 paper: DiffusionTrack.

:star: We release the bounding box implementation of the DiffusionTrack. More is coming.

Abstract

DiffusionTrack_pipeline

Existing Siamese or transformer trackers commonly pose visual object tracking as a one-shot detection problem, i.e., locating the target object in a \textbf{single forward evaluation} scheme. Despite the demonstrated success, these trackers may easily drift towards distractors with similar appearance due to the single forward evaluation scheme lacking self-correction. To address this issue, we cast visual tracking as a point set based denoising diffusion process and propose a novel generative learning based tracker, dubbed DiffusionTrack. Our DiffusionTrack possesses two appealing properties: 1) It follows a novel noise-to-target tracking paradigm that leverages \textbf{multiple} denoising diffusion steps to localize the target in a dynamic searching manner per frame. 2) It models the diffusion process using a point set representation, which can better handle appearance variations for more precise localization. One side benefit is that DiffusionTrack greatly simplifies the post-processing, e.g., removing the window penalty scheme. Without bells and whistles, our DiffusionTrack achieves leading performance over the state-of-the-art trackers and runs in real-time.

Highlights

A Generative paradigm

DiffusionTrack_pipeline

DiffusionTrack has an encoder-decoder structure. The encoder extracts target-aware features and feeds search features into the decoder. The decoder, comprising of a stack of diffusion layers, refines the point set groups to localize the target.

Head_Framework

Details of a diffusion layer. It consists of three components: 1) Global instance layer: it produces target proposals in a generative style and models the instance-level relationship. 2) Dynamic conv layer: it performs dynamic convolution with instance features. 3) Refinement layer: it refines the point sets and estimates corresponding confidence scores

Install the environment

conda create -n seqtrack python=3.8
conda activate diffusintrack
bash install.sh

Our codebase is built on top of Detectron2, you need install detectron2 first.

git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2

Add the project path to environment variables

export PYTHONPATH=<absolute_path_of_DiffusionTrack>:$PYTHONPATH

Data Preparation

Put the tracking datasets in ./data. It should look like:

${SeqTrack_ROOT}
 -- data
     -- lasot
         |-- airplane
         |-- basketball
         |-- bear
         ...
     -- got10k
         |-- test
         |-- train
         |-- val
     -- coco
         |-- annotations
         |-- images
     -- trackingnet
         |-- TRAIN_0
         |-- TRAIN_1
         ...
         |-- TRAIN_11
         |-- TEST

Set project paths

Run the following command to set paths for this project

python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir .

After running this command, you can also modify paths by editing these two files

lib/train/admin/local.py  # paths about training
lib/test/evaluation/local.py  # paths about testing

Train DiffusionTrack

python -m torch.distributed.launch --nproc_per_node 8 lib/train/run_training_diffusiontrack.py --script diffusiontrack --config diffusiontrack_b256 --save_dir ./output/diffusiontrack_b256

Test and evaluate on benchmarks

LaSOT

python tracking/test.py diffusiontrack diffusiontrack_b256 --dataset lasot --threads 2
python tracking/analysis_results.py # need to modify tracker configs and names

GOT10K-test

python tracking/test.py seqtrack diffusiontrack_b256_got --dataset got10k_test --threads 2
python lib/test/utils/transform_got10k.py --tracker_name diffusiontrack --cfg_name diffusiontrack_b256_got

TrackingNet

python tracking/test.py diffusiontrack diffusiontrack_b256 --dataset trackingnet --threads 2
python lib/test/utils/transform_trackingnet.py --tracker_name diffusiontrack --cfg_name diffusiontrack_b256

UAV123

python tracking/test.py diffusiontrack diffusiontrack_b256 --dataset uav --threads 2
python tracking/analysis_results.py # need to modify tracker configs and names

Test FLOPs, Params, and Speed

python tracking/profile_model.py --script diffusiontrack --config diffusiontrack_b256

Acknowledgement

This codebase is implemented on DiffusionDet, SeqTrack, and PyTracking libraries. We would like to thank their authors for providing great libraries.

Citation

If our work is useful for your research, please consider citing:

@InProceedings{Xie_2024_CVPR,
    author    = {Xie, Fei and Wang, Zhongdao and Ma, Chao},
    title     = {DiffusionTrack: Point Set Diffusion Model for Visual Object Tracking},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
}