yzlicloud's repositories
DQN-based-UAV-3D_path_planer
Realization of UAV's Track Planning in 3D Urban Environment Based on Reinforcement Learning Algorithm(DQN)
toolkit
Official Python toolkit for generic object tracking benchmark GOT-10k and beyond
CVPR2022-Paper-Code-Interpretation
cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集，极市团队整理
multi_agent_path_planning
Python implementation of a bunch of multi-robot path-planning algorithms.
FUEL
An Efficient Framework for Fast UAV Exploration
Maml-Reptile-RL
Study of paper "Meta reinforcement learning for sim-to-real domain adaptation"
mmtracking
OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.
Single_Object_Tracking_Paper_List
Paper list for single object tracking (State-of-the-art SOT trackers)
uav_data_harvesting
Python implementation of DDQN multi-UAV data harvesting
Matlab-Code
Personal matlab code for many years, use must be quoted
active_tracking_rl
Active visual tracking library based on PyTorch.
ICCV2021-Papers-with-Code
ICCV 2021 论文和开源项目合集
Hough_zhizhenshibie_MATLAB
本设计为基于MATLAB的表盘指针识别，算法原理是基于hough变换。可检测压力表，石英手表，电表刻度，气压表等带指针刻度的表盘。通过hough检测直线和圆的关系，得出指针夹角，根据刻度换算关系得出具体刻度值。算法流程为：原图，灰度变换，二值化，hough变换，刻度指针处刻度定位，计算夹角，得出示数。本设计带有一个人机交互GUI界面，操作人性化，逻辑清晰。
few-shot-meta-baseline
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning, in ICCV 2021
FTMU
Fast Template Matching and Update for Video Object Tracking and Segmentation
SNCOVT
a moderate and simulated space non-cooperative object visual tracking dataset, which contains 60 binocular video sequences with manual annotations, 54742 frames in total.
rl-experiments
Keeping track of RL experiments
MPN
Official codes of CVPR21 paper: Learning Normal Dynamics in Videos with Meta Prototype Network
PathPlanning
Common used path planning algorithms with animations.
shuzibiaopanshibie_matlab
该课题为MATLAB数字仪表图像识别系统。可以识别万用表，压力表，电表，手表等的数字识别，包括小数点。带有一个GUI界面，流程为：灰度，二值化，定位，连通闭环，分割出数字区域，开闭运算，去除小面积，细化，精准定位，分割，识别。
meter-detector
工业仪表读数识别
fastai
The fastai deep learning library, plus lessons and tutorials
vot.py
Fixing vot.py to integrate it with python