Rostyslav Ivasiv's repositories
chffr-api
API to access chffr data!
ofp
OpenFastPath project
CVCalendar
A custom visual calendar for iOS 8+ written in Swift (2.0).
stronglink
A searchable, syncable, content-addressable notetaking system
Road_Detection_BCD_Spline
This repository contains the codes & results concerning the detection of lanes using BCD and Spline fitting
commacoloring
you like coloring books?
BuildingMachineLearningSystemsWithPython
Source Code for the book Building Machine Learning Systems with Python
cv-lane
Computer Vision for autonomous lane detection (and motor control)
SAI
Switch Abstraction Interface
lane-detection-2
Using OpenCV to detect lanes
LaneDetection-
CSE396
ITS
experiments about automobile vision, now focusing on lane marking/boundary detection & tracking. see more https://github.com/baidut/OpenVehicleVision
TranslateBookLearningOpenCV
Перевод книги "Learning OpenCV" на русский язык (любительский)
LaneTracking
Lane detection and tracking
self-driving-car
Cloned a repo that handles lane detection... can I make a self driving car?
go
The Go programming language
free-programming-books
:books: Freely available programming books
awesome-microservices
A curated list of Microservice Architecture related principles and technologies.
LaneDetection-5
This code is improved version of code written by Mohamed Aly California Institute of Technology for road detection.
lane_detection-2
Lane Detection and Particle Filter Tracking
lane_detect
Lane detection using a Kalman filter and Hough line transform
ardunimo
Nim wrapper using c2nim for Arduino and MediaTek LinkIt One development
Object-Detection
Introduction The project presents an efficient method for object detection and demonstrates its use for real-time vision on-board vehicles. The work is closely based on Paul Viola’s face detection method [1], except that the system here was trained to detect objects related to traffic in order to assist the auto pilot on smart vehicles. The idea was adapted from D M Gavrila’s paper [2] REAL-TIME OBJECT DETECTION FOR "SMART" VEHICLES. Objective The target is to create Haar feature-based cascade classifiers by training the system with an appropriate amount of traffic related information in order to detect vehicles, pedestrians and traffic signs in real-time. Thus, this system in addition to driving assistance/collision avoidance, could further find its application in tracking and recognition.