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trackerdsst

Tracker based on DSST

traffic_sign_detection

tusimple-benchmark

TuSimple Benchmark

ultra_high_resolution_segmentation

code used for CVPR2019 oral "Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images"

unsupervised_llamas

Code for https://unsupervised-llamas.com

urbanloco

UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes

vdo_slam

VDO-SLAM: A Visual Dynamic Object-aware SLAM System

vins-mono-code-annotation

VINS-Mono代码注释以及公式推导

vins-mono-learning

VINS-Mono代码注释，仅供学习

vision4med

notes of seminal papers on segmentation and detection using deep learning, i.e. CNN, GANs, to process 3D and 4D medical images and improve the workflow of radiologists, radiation oncologists and surgeons.

vo-slam-review

SLAM is mainly divided into two parts: the front end and the back end. The front end is the visual odometer (VO), which roughly estimates the motion of the camera based on the information of adjacent images and provides a good initial value for the back end.The implementation methods of VO can be divided into two categories according to whether features are extracted or not: feature point-based methods, and direct methods without feature points. VO based on feature points is stable and insensitive to illumination and dynamic objects

voldor

VOLDOR-SLAM is a real-time dense-indirect SLAM system takes dense optical flows as input that supports monocular, stereo and RGB-D video sequence.

weed-mapping

Weed Mapping in Aerial Images through Identification and Segmentation of Crop Rows and Weeds using Convolutional Neural Networks

weightwatcher

The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

yolo_segmentation

A Frankenstein remix of YOLO and Image Segmentation Suite for Ultimate Computer Vision Tasks