vehicle_detection_haarcascades - Vehicle Detection by Haar Cascades with OpenCV

  •        12

Hello everyone, An easy way to perform vehicle detection is by using Haar Cascades. Currently, I don't have a detailed tutorial about it, but you can get some extra information in the OpenCV homepage, see Cascade Classifier page. See also Cascade Classifier Training for training your own cascade classifier. The haar-cascade cars.xml was trained using 526 images of cars from the rear (360 x 240 pixels, no scale). The images were extracted from the Car dataset proposed by Brad Philip and Paul Updike taken of the freeways of southern California.

https://github.com/andrewssobral/vehicle_detection_haarcascades

Tags
Implementation
License
Platform

   




Related Projects

simple_vehicle_counting - Vehicle Detection, Tracking and Counting

  •    C++

Note: the procedure is similar for OpenCV 2.4.x and Visual Studio 2013. Go to Windows console.

CarND-Vehicle-Detection - Vehicle detection using YOLO in Keras runs at 21FPS

  •    Jupyter

This is a project for Udacity self-driving car Nanodegree program. The aim of this project is to detect the vehicles in a dash camera video. The implementation of the project is in the file vehicle_detection.ipynb. This implementation is able to achieve 21FPS without batching processing. The final video output is here. In this README, each step in the pipeline will be explained in details.

cascade-rcnn - Caffe implementation of multiple popular object detection frameworks

  •    C++

This repository is written by Zhaowei Cai at UC San Diego. This repository implements mulitple popular object detection algorithms, including Faster R-CNN, R-FCN, FPN, and our recently proposed Cascade R-CNN, on the MS-COCO and PASCAL VOC datasets. Multiple choices are available for backbone network, including AlexNet, VGG-Net and ResNet. It is written in C++ and powered by Caffe deep learning toolbox.

opencv-haar-classifier-training - Learn how to train your own OpenCV Haar classifier

  •    Perl

This repository aims to provide tools and information on training your own OpenCV Haar classifier. Use it in conjunction with this blog post: Train your own OpenCV Haar classifier. Note: If you get the error struct.error: unpack requires a string argument of length 12 then go into your samples directory and delete all files of length 0.

SeetaFaceEngine

  •    C++

SeetaFace Engine is an open source C++ face recognition engine, which can run on CPU with no third-party dependence. It contains three key parts, i.e., SeetaFace Detection, SeetaFace Alignment and SeetaFace Identification, which are necessary and sufficient for building a real-world face recognition applicaiton system. SeetaFace Detection implements a funnel-structured (FuSt) cascade schema for real-time multi-view face detection, which achieves a good trade-off between detection accuracy and speed. State of the art accuracy can be achieved on the public dataset FDDB in high speed. See SeetaFace Detection for more details.


awesome-vehicle-security - 🚗 A curated list of resources for learning about vehicle security and car hacking

  •    

A curated list of awesome resources, books, hardware, software, applications, people to follow, and more cool stuff about vehicle security, car hacking, and tinkering with the functionality of your car. Follow me on Twitter for more security goodness.

CarND-Vehicle-Detection - Vehicle Detection Project

  •    Shell

In this project, your goal is to write a software pipeline to detect vehicles in a video (start with the test_video.mp4 and later implement on full project_video.mp4), but the main output or product we want you to create is a detailed writeup of the project. Check out the writeup template for this project and use it as a starting point for creating your own writeup. A great writeup should include the rubric points as well as your description of how you addressed each point. You should include a detailed description of the code used in each step (with line-number references and code snippets where necessary), and links to other supporting documents or external references. You should include images in your writeup to demonstrate how your code works with examples.

oscc - Open Source Car Control 💻🚗🙌

  •    C++

Open Source Car Control (OSCC) is an assemblage of software and hardware designs that enable computer control of modern cars in order to facilitate the development of autonomous vehicle technology. It is a modular and stable way of using software to interface with a vehicle’s communications network and control systems. OSCC enables developers to send control commands to the vehicle, read control messages from the vehicle’s OBD-II CAN network, and forward reports for current vehicle control state. Such as steering angle & wheel speeds. Control commands are issued to the vehicle component ECUs via the steering wheel torque sensor, throttle position sensor, and brake position sensor. (Because the gas-powered Kia Soul isn’t brake by-wire capable, an auxiliary actuator is added to enable braking.) This low-level interface means that OSCC offers full-range control of the vehicle without altering the factory safety-case, spoofing CAN messages, or hacking ADAS features.

sdl_core - SmartDeviceLink In-Vehicle Software and Sample HMI

  •    C++

SmartDeviceLink (SDL) is a standard set of protocols and messages that connect applications on a smartphone to a vehicle head unit. This messaging enables a consumer to interact with their application using common in-vehicle interfaces such as a touch screen display, embedded voice recognition, steering wheel controls and various vehicle knobs and buttons. There are three main components that make up the SDL ecosystem. The Core component of SDL runs on a vehicle's computing system (head unit). Core’s primary responsibility is to pass messages between connected smartphone applications and the vehicle HMI, and pass notifications from the vehicle to those applications. It can connect a smartphone to a vehicle's head unit via a variety of transport protocols such as Bluetooth, USB, Android AOA, and TCP. Once a connection is established, Core discovers compatible applications and displays them to the driver for interaction via voice or display. The core component is implemented into the vehicle HMI based on the integration guidelines above. The core component is configured to follow a set of policies defined in a policy database and updated by a policy server. The messaging between a connected application and core is defined by the Mobile API and the messaging between sdl core and the vehicle is defined by the HMI API.

CarND-LaneLines-P1 - Lane Finding Project for Self-Driving Car ND

  •    Jupyter

When we drive, we use our eyes to decide where to go. The lines on the road that show us where the lanes are act as our constant reference for where to steer the vehicle. Naturally, one of the first things we would like to do in developing a self-driving car is to automatically detect lane lines using an algorithm. In this project you will detect lane lines in images using Python and OpenCV. OpenCV means "Open-Source Computer Vision", which is a package that has many useful tools for analyzing images.

chrisify - Adds some much needed Chris to an image.

  •    Go

If executed from any location besides the repository, you must tell it where to find the bundled Haar Cascade face recognition XML file. I tried to bundle it with the binary, but it must be provided as a file to the OpenCV library, so a file path is necessary.

object-detector - Object Detection Framework using HOG as descriptor and Linear SVM as classifier.

  •    Python

I have created a single python script that can be used to test the code. To test the code, run the lines below in your terminal. The test-object-detector will download the UIUC Image Database for Car Detection and train a classifier to detect cars in an image. The SVM model files will be stored in data/models, so that they can be resused later on.

Car Bloke - WP7

  •    

Car Bloke is a vehicle mileage tracker, vehicle maintenance log, and vehicle cost log for Windows Phone 7.

laravel-soft-cascade - Cascade Delete & Restore when using Laravel SoftDeletes

  •    PHP

Cascade delete and restore when using the Laravel or Lumen SoftDeletes feature. If you enjoy features like MySQL cascade deleting but want to use Laravels SoftDeletes feature you'll need to do some extra steps to ensure your relations are properly deleted or restored.

face_detect_n_track - Fast and robust face detection and tracking

  •    C++

First you need to create a VideoCapture object that you'll use as a source. Then pass the path to your cascade file along with the VideoCapture object to the VideoFaceDetector.

opencv4nodejs - Asynchronous OpenCV 3

  •    C++

By its nature, JavaScript lacks the performance to implement Computer Vision tasks efficiently. Therefore this package brings the performance of the native OpenCV library to your Node.js application. This project targets OpenCV 3 and provides an asynchronous as well as an synchronous API. The ultimate goal of this project is to provide a comprehensive collection of Node.js bindings to the API of OpenCV and the OpenCV-contrib modules. An overview of available bindings can be found in the API Documentation. Furthermore, contribution is highly appreciated. If you want to get involved you can have a look at the contribution guide.

CELL Facedetect

  •    C

Fast Haar-like object detection develop to CELL processor based plataform. This is reimplementing stump based algorithm of OpenCV library.

lbpcascade_animeface - A Face detector for anime/manga using OpenCV

  •    

The face detector for anime/manga using OpenCV. Download and place the cascade file into your project directory.

libface - Face Recognition Library

  •    C++

Libface is a cross platform framework for developing face recognition algorithms and testing its performance. The library uses OpenCV 2.0 and aims to be a middleware for developers that don’t have to include any OpenCV code in order to use face recognition and face detection detection.

EV Dashboard

  •    

EV Dashboard is Windows CE 5.0 and Windows Mobile app to monitor and manage Electric Vehicle operation.