Official code for 'Robust Siamese Object Tracking for Unmanned Aerial Manipulator' and offical introduction to UAMT100 benchmark

Related tags

Deep LearningSiamSA
Overview

SiamSA: Robust Siamese Object Tracking for Unmanned Aerial Manipulator

Demo video

  • 📹 Our video on Youtube and bilibili demonstrates the evaluation of SiamSA and other 4 state-of-the-art trackers on [email protected] and UAMT100 benchmark.

SiamSA

  • 📹 ​Real-world tests of SiamSA on a flying UAM platform form first and third perspective are also involved.

UAMT100 benchmark

  • The UAMT100 benchmark consists of 100 image sequences, which are captured from UAM perspectives. For subsequent tasks of UAM tracking, such as grasping, it represents various possibilities of UAM's tracking the object in an indoor environment.

image-20210915230200440

  • 16 kinds of objects are involved, and 11 attributes are annotated for each sequence. The figure demonstrates four scenarios of UAM tracking in UAMT100. The histogram in the figure is a statistic of attributes in UAMT100.
  • For more detail, please refer to the benchmark website, which will be released soon.

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:

pip install -r requirements.txt

Test

Download model from Google Drive or BaiduYun (code: v4r0) and put it into tools/snapshot directory.

Download testing datasets and put them into test_dataset directory. If you want to test the tracker on a new dataset, please refer to pysot-toolkit to set test_dataset.

python test.py 	                    \
	--trackername SiamSA            \ # tracker_name
	--dataset UAV123_10fps          \ # dataset_name
	--snapshot snapshot/model.pth     # model_path

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

We provide our test results on Google Drive and BaiduYun (code: v4r1).

Train

Prepare training datasets

Download the datasets:

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

Train a model

To train the SiamSA model, run train.py with the desired configs:

cd tools
python train.py 

Evaluation

If you want to evaluate the tracker mentioned above, please put those results into results directory.

python eval.py 	                      \
	--tracker_path ./results          \ # result path
	--dataset UAV123_10fps            \ # dataset_name
	--tracker_prefix 'model'            # tracker_name

Contact

If you have any questions, please contact me.

Guangze Zheng

Email: [email protected]

Acknowledgement

  • The code is implemented based on pysot and SiamAPN. We would like to express our sincere thanks to the contributors.
  • Besides, we would like to thank Ziang Cao for his advice on the code.
  • As for UAMT100 benchmark, we appreciate the help from Fuling Lin, Haobo Zuo, and Liangliang Yao.
  • We would like to thank Kunhan Lu for his advice on TensorRT acceleration.
Owner
Intelligent Vision for Robotics in Complex Environment
Adaptive Vision for Robotics in Complex Environment
Intelligent Vision for Robotics in Complex Environment
🦕 NanoSaur is a little tracked robot ROS2 enabled, made for an NVIDIA Jetson Nano

🦕 nanosaur NanoSaur is a little tracked robot ROS2 enabled, made for an NVIDIA Jetson Nano Website: nanosaur.ai Do you need an help? Discord For tech

NanoSaur 162 Dec 09, 2022
Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution

unfoldedVBA Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution This repository contains the Pytorch implementation of the unrolled

Yunshi HUANG 2 Jul 10, 2022
[CVPR 2022 Oral] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

EPro-PnP EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation In CVPR 2022 (Oral). [paper] Hanshen

同济大学智能汽车研究所综合感知研究组 ( Comprehensive Perception Research Group under Institute of Intelligent Vehicles, School of Automotive Studies, Tongji University) 842 Jan 04, 2023
Self-Supervised Image Denoising via Iterative Data Refinement

Self-Supervised Image Denoising via Iterative Data Refinement Yi Zhang1, Dasong Li1, Ka Lung Law2, Xiaogang Wang1, Hongwei Qin2, Hongsheng Li1 1CUHK-S

Zhang Yi 72 Jan 01, 2023
Minimal implementation and experiments of "No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging".

No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging Minimal implementation and experiments of "No-Transaction Band N

19 Jan 03, 2023
Simple tools for logging and visualizing, loading and training

TNT TNT is a library providing powerful dataloading, logging and visualization utilities for Python. It is closely integrated with PyTorch and is desi

1.5k Jan 02, 2023
PyTorch implementation of DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images

DARDet PyTorch implementation of "DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images", [pdf]. Highlights: 1. We develop a new dense

41 Oct 23, 2022
Implementation of "DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing".

DeepOrder Implementation of DeepOrder for the paper "DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing". Project

6 Nov 07, 2022
This is the source code for generating the ASL-Skeleton3D and ASL-Phono datasets. Check out the README.md for more details.

ASL-Skeleton3D and ASL-Phono Datasets Generator The ASL-Skeleton3D contains a representation based on mapping into the three-dimensional space the coo

Cleison Amorim 5 Nov 20, 2022
Unsupervised Feature Ranking via Attribute Networks.

FRANe Unsupervised Feature Ranking via Attribute Networks (FRANe) converts a dataset into a network (graph) with nodes that correspond to the features

7 Sep 29, 2022
RepVGG: Making VGG-style ConvNets Great Again

RepVGG: Making VGG-style ConvNets Great Again (PyTorch) This is a super simple ConvNet architecture that achieves over 80% top-1 accuracy on ImageNet

2.8k Jan 04, 2023
PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training”

A new codebase for popular Scene Graph Generation methods (2020). Visualization & Scene Graph Extraction on custom images/datasets are provided. It's also a PyTorch implementation of paper “Unbiased

Kaihua Tang 824 Jan 03, 2023
Semi-supervised Domain Adaptation via Minimax Entropy

Semi-supervised Domain Adaptation via Minimax Entropy (ICCV 2019) Install pip install -r requirements.txt The code is written for Pytorch 0.4.0, but s

Vision and Learning Group 243 Jan 09, 2023
Dense Gaussian Processes for Few-Shot Segmentation

DGPNet - Dense Gaussian Processes for Few-Shot Segmentation Welcome to the public repository for DGPNet. The paper is available at arxiv: https://arxi

37 Jan 07, 2023
Zero-shot Synthesis with Group-Supervised Learning (ICLR 2021 paper)

GSL - Zero-shot Synthesis with Group-Supervised Learning Figure: Zero-shot synthesis performance of our method with different dataset (iLab-20M, RaFD,

Andy_Ge 62 Dec 21, 2022
Convolutional Neural Network for Text Classification in Tensorflow

This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post. It is slightly simplified implementation of Kim's Convo

Denny Britz 5.5k Jan 02, 2023
LightningFSL: Pytorch-Lightning implementations of Few-Shot Learning models.

LightningFSL: Few-Shot Learning with Pytorch-Lightning In this repo, a number of pytorch-lightning implementations of FSL algorithms are provided, inc

Xu Luo 76 Dec 11, 2022
InterfaceGAN++: Exploring the limits of InterfaceGAN

InterfaceGAN++: Exploring the limits of InterfaceGAN Authors: Apavou Clément & Belkada Younes From left to right - Images generated using styleGAN and

Younes Belkada 42 Dec 23, 2022
Gradient-free global optimization algorithm for multidimensional functions based on the low rank tensor train format

ttopt Description Gradient-free global optimization algorithm for multidimensional functions based on the low rank tensor train (TT) format and maximu

5 May 23, 2022
A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''.

P-tuning A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''. How to use our code We have released the code

THUDM 562 Dec 27, 2022