drzhangreid's starred repositories
awesome-infrared-small-targets
List of awesome infrared small targets detection methods!
Stone-Soup
A software project to provide the target tracking community with a framework for the development and testing of tracking algorithms.
mul_radar_fus
多雷达航迹关联
MDvsFA_cGAN
The tensorflow and pytorch implementations of the MDvsFA_cGAN model which is proposed in ICCV2019 paper "Huan Wang, Luping Zhou and Lei Wang. Miss Detection vs. False Alarm: Adversarial Learing for Small Object Segmentation in Infrared Images. International Conference on Computer Vision, Oct.27-Nov.2,2019. Seoul, Republic of Korea".
unsupervisedDeepHomography-pytorch
Pytorch implementation of Unsupervised Deep Homography.
DeepHomography
Content-Aware Unsupervised Deep Homography Estimation
unsupervisedDeepHomographyRAL2018
Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model
SpectralResidualSaliency
C++/Python implementation of spectral residual saliency detection algorithm
Parallel_ViBe
ViBe : background substraction algorithm
Anti-UAV
Served as a large-scale multi-modal benchmark, Anti-UAV drives the future research on the frontiers of tracking UAVs in the wild. With the above innovations and contributions, we have organized the CVPR 2020 Workshop on the 1st Anti-UAV Challenge. These contributions together significantly benefit the community.
AndroidInterView
Android面试2019年最新版（每日更新），音视频，Android高级，性能优化，算法，Flutter技术，FFmpeg OppenGl，资源混淆,插件化，组件化，OkHttp，Rxjava,架构师，Android架构
interview
📚 C/C++ 技术面试基础知识总结，包括语言、程序库、数据结构、算法、系统、网络、链接装载库等知识及面试经验、招聘、内推等信息。This repository is a summary of the basic knowledge of recruiting job seekers and beginners in the direction of C/C++ technology, including language, program library, data structure, algorithm, system, network, link loading library, interview experience, recruitment, recommendation, etc.
algorithm-pattern
算法模板，最科学的刷题方式，最快速的刷题路径，你值得拥有~
awesome-programming-books-1
计算机经典书籍📚，保留书单
Saliency-Detection-Algorithm-via-Multi-Level-Graph-Structure-and-Accurate-Background-Queries-Select
In the field of saliency detection, many graph-based algorithms use boundary pixels as background seeds to estimate the background and foreground saliency,which leads to significant errors in some of pictures. In addition, local context with high contrast will mislead the algorithms. In this paper, we propose a novel multilevel bottom-up saliency detection approach that accurately utilizes the boundary information and takes advantage of both region-based features and local image details. To provide more accurate saliency estimations, we build a three-level graph model to capture both region-based features and local image details. By using superpixels of all four boundaries, we first roughly figure out the foreground superpixels. After calculating the RGB distances between the average of foreground superpixels and every boundary superpixel, we discard the boundary superpixels with the longest distance to get a set of accurate background boundary queries. Finally, we propose the regularized random walks ranking to formulate pixel-wise saliency maps. Experiment results on two public datasets indicate the significantly promoted accuracy and robustness of our proposed algorithm in comparison with 7 state-of-the-art saliency detection approaches.
unsupervised_detection
An Unsupervised Learning Framework for Moving Object Detection From Videos