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