whjzsy / TGSR

This is the official implementation with training code for 'Trajectory Guided Robust Visual Object Tracking with Selective Remedy'.

TIR

This is the official implementation with training code for 'Trajectory Guided Robust Visual Object Tracking with Selective Remedy'.

Introduction

We propose a fast yet robust tracking algorithm with two light-load novel modules: Trajectory Guidance Module (TGM) and Selective Refinement Module (SRM). Specifically, TGM encourages to pay more attention on possible target location based on historical trajectory. SRM selectively remedies the tracking results at the risk of failure with little impact on the speed. The proposed algorithm can be easily incorporated into existing Siamese trackers and obtains the state-of-the-art performance on six benchmarks with high real-time tracking speed.

Installation

Please refer to PySOT_INSTALL.md and PreciseRoIPooling_README.md for installation.

Due to PreciseRoIPooling, PLEASE USE THE COMMAND TO DOWNLOADE THE CODE: git clone https://github.com/TJUMMG/TGSR.git

Requirements

1. Ubuntu 20.04
2. Pytorch 1.3.1
3. Python 3.7

Usage

Download models and tracking results

Baidupan, keyword: 9tu5

The folder has five files:

• result.zip : the tracking result on the six benchmarks
• research.zip : the tracking result for ALTL, should be unzipped to './pioneer/research/'
• snapshot_test.zip : the model of TGSR, should be unzipped to './snapshot_test'
• cxcy_uav123.txt : the training data for TPN, should be unzipped to './pioneer/data/'
• experiments.zip : the model of SiamRPN++ and SiamMask, should be unzipped to './experiments'

Eval ALTL

1. run the './pioneer/research/eval_tool.py'

Test

1. Modify the dataset path 'dataset_root'.
2. run the './tools/test_SiamRPN++_VOT.py'.

Train

1. run the './pioneer/traj_predict_train.py' to train TPN
2. run the './pioneer/IoU_train.py' to train IPN
3. run the './pioneer/Refine_train.py' to train BRN

Acknowledgments

1. PySOT
2. pytracking
3. PreciseRoIPooling_README.md
4. DR_Loss