Pinned Repositories
11_GPS_LiDAR_NLOS_C
Sensor fusion with Improved GNSS positioning
ademxapp
Code for https://arxiv.org/abs/1611.10080
Advance-Lane-Finding
Implementing advance lane finding using computer vision
Advanced-Lane-Detection
Model for the extraction of lane lines, both curved and straight, from the road. Augmented the lane area, as well added important metrics, such as cars distance from the center of the road. Implemented with OpenCV and python.
advanced_lane_detection
Advanced lane detection using computer vision
alienware15r3_ubuntu14
Instructions on how to install Ubuntu 14.04 on the Alienware 15 R3 (and installing cuda, tensorflow and disabling intel graphics card)
apollo
An open autonomous driving platform
HDMAP-ORB-SLAM
SuperPointPretrainedNetwork
switchui
aicarmark's Repositories
aicarmark/ademxapp
Code for https://arxiv.org/abs/1611.10080
aicarmark/Advance-Lane-Finding
Implementing advance lane finding using computer vision
aicarmark/Advanced-Lane-Detection
Model for the extraction of lane lines, both curved and straight, from the road. Augmented the lane area, as well added important metrics, such as cars distance from the center of the road. Implemented with OpenCV and python.
aicarmark/advanced_lane_detection
Advanced lane detection using computer vision
aicarmark/alienware15r3_ubuntu14
Instructions on how to install Ubuntu 14.04 on the Alienware 15 R3 (and installing cuda, tensorflow and disabling intel graphics card)
aicarmark/caffe-segnet-cudnn5
This repository was a fork of BVLC/caffe and includes the upsample, bn, dense_image_data and softmax_with_loss (with class weighting) layers of caffe-segnet (https://github.com/alexgkendall/caffe-segnet) to run SegNet with cuDNN version 5.
aicarmark/CarND-Vehicle-Detection-1
Vehicle detection using YOLO in Keras runs at 21FPS
aicarmark/Deep-Learning-for-Medical-Applications
Deep Learning Papers on Medical Image Analysis
aicarmark/fcn.berkeleyvision.org
Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.
aicarmark/Gentle_Intro_CV
CS701
aicarmark/hosts
:statue_of_liberty:最新可用的google hosts文件。镜像:
aicarmark/KittiSeg
A Kitti Road Segmentation model implemented in tensorflow.
aicarmark/MultiCol-SLAM
This repository contains a multi-fisheye camera SLAM. The underlying SLAM system is based on ORB-SLAM.
aicarmark/Object-Tracking-using-UKF-by-Fusing-Lidar-and-Radar
Object Tracking using UKF by Fusing Lidar and Radar Data
aicarmark/opencv_contrib
Repository for OpenCV's extra modules
aicarmark/OpenVehicleVision
An opensource lib. for vehicle vision applications (written by MATLAB), lane marking detection, road segmentation
aicarmark/OSCC
Open Source Car Control 💻🚗🙌
aicarmark/PipeCNN
An OpenCL-based FPGA Accelerator for Convolutinal Neural Networks
aicarmark/PSPNet
Repository for paper https://arxiv.org/abs/1612.01105
aicarmark/py-faster-rcnn
Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version
aicarmark/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
aicarmark/resmatch
Implementation of "Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning"
aicarmark/road_lane_line_detection
Find lane lines on the road using Python and OpenCV, applying Canny edge detectors and Hough line transforms
aicarmark/segnet
SegNet-like Autoencoders in TensorFlow
aicarmark/SegNet-Tutorial
Files for a tutorial to train SegNet for road scenes using the CamVid dataset
aicarmark/Tensorflow-DeconvNet-Segmentation
Tensorflow implementation of "Learning Deconvolution Network for Semantic Segmentation"
aicarmark/tensorflow-fcn
An Implementation of Fully Convolutional Networks in Tensorflow.
aicarmark/tensorflow-yolo
tensorflow implementation of 'YOLO : Real-Time Object Detection'(train and test)
aicarmark/TFFRCNN
FastER RCNN built on tensorflow
aicarmark/Vehicle-Detection-and-Tracking
This is a deep learing approach to Udacitys "Vehicle Detection and Tracking" project in the CarND using CNNs.