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Detect and Track Objects

Posted by Hemprasad Y. Badgujar on January 20, 2015


Detect and Track Objects With OpenCV

In the following, I made an overview of tutorials and guides to getting strted how to use OpenCV for detection and tracking objects. OpenCV is a library for computer visions designed for analyze, process, and understand the objects from images aiming to produce information.

  • OpenCV Tutorials – comprehensive list with basic OpenCV tutorials and source code based on the OpenCV library;
  • Object Detection & Tracking Using Color – example of application where OpenCV is used to detect objects based on color differences;
  • Face Detection Using OpenCV – guide how to use OpenCV to detect one or more faces from the same image;
  • SURF in OpenCV – tutorial how to use SURF algorithm designed to detect key-points and descriptors in images;
  • Introduction to Face Detection and Face Recognition – face detection and recognition are two of the most common applications in computer vision from robotics, and this tutorial present the steps how a face is detected and recognized from images;
  • Find Objects with a Webcam – using a simple webcam mounted on a robot and the Simple Qt interface designed to work with OpenCV, as you can see in this tutorial any object can be detected and tracked in images;
  • Features 2D + Homography to Find a Known Object – tutorial with programming code and explanation in order to use two important functions included in OpenCV. These two functions – findHomography and perspectiveTransform – are used to find objects in images. The findHomography is a function based on a technique called Key-point Matching, while the perspectiveTransform is an advanced class capable of mapping the points from an image;
  • Back Projection – tutorial based on calcBackProject function designed to calculate the back project of the histogram;
  • Tracking Colored Objects in OpenCV – tutorial for detection and tracking the colored objects from images using the OpenCV library;
  • OpenCV Tutorials – Based on “Learning OpenCV – Computer Vision with the OpenCV Library” – in order to be familiar with computer vision concepts, these tutorials can be useful for beginner and advanced users to start building applications or to improve the skills;
  • Image Processing on Pandaboard using OpenCV and Kinect – in this presentation you can find information about image processing with a Pandaboard single board computer using the Kinect sensor and the OpenCV library;
  • Video Capture using OpenCV with VC++ – OpenCV library can be integrated with Visual Studio and this article explain you as a programmer how to use the Visual C++ together with OpenCV;

Tutorials for Detecting and Tracking Objects with Mobile Devices

Mobile devices such as smartphones and tablets with iOS or Android operating systems can be integrated into robots and used to detect and track objects. Below is an overview of tutorials with comprehensive information for tracking objects using different mobile devices.

 Particle filter based trackers

  • Particle Filter Color Tracker [Link 1]
    • Matlab and c/c++ code.
    • Key words: region tracker, color histogram, ellipsoidal region, particle filter, SIR resampling.
  • Region Tracker based on a color Particle Filter [Link 1] [Example]
    • Matlab and c/c++ code.
    • Key words: region tracker, color histogram, ellipsoidal region, particle filter, SIR resampling.
  • Region Tracker based on an intensity Particle Filter [Link]
    • Matlab and c/c++ code.
    • Key words: region tracker, intensity histogram, ellipsoidal region, particle filter, SIR resampling.
  • Particle Filter Object Tracking [Link]
    • C/C++.

Mean shift based trackers

  • Scale and Orientation Adaptive Mean Shift Tracking. [Link]
    • Matlab.
  • Robust Mean Shift  Tracking with Corrected Background-Weighted Histogram. [Link]
    • Matlab.
  • Robust Object Tracking using Joint Color-Texture Histogram. [Link]
    • Matlab.
  • Tracking moving video objects using mean-shift algorithm. [Link]
    • Matlab.
  • Mean-shift Based Moving Object Tracker [Link]
    • C/C++.
  • Mean-Shift Video Tracking [Link]
    • Matlab.
  • Gray scale mean shift algorithm for tracking. [Link]
    • Matlab.
  • Mean shift tracking algorithm for tracking [Link]
    • Matlab.

Deformable/articulable object trackers

  • Visual Tracking with Integral Histograms and Articulating Blocks [Link]
    • Matlab and c/c++ code
    • Key words: region tracker, intensity histogram, multi-rectangular regions, integral histogram, exhaustive search, graph cut segmentation.

Appearance learning based trackers

  • Robust Object Tracking with Online Multiple Instance Learning. [Link]
    • C/C++.
  • Visual Tracking via Adaptive Structural Local Sparse Appearance Model. [Link]
    • Matlab.
  • Online Discriminative Object Tracking with Local Sparse Representation. [Link]
    • Matlab
  • Superpixel Tracking. [Link]
    • Matlab.
  • Online Multiple Support Instance Tracking. [Link]
    • Matlab.
  • Incremental Learning for Robust Visual Tracking. [Link]
    • Matlab.
  • Tracking with Online Multiple Instance Learning (MILTrack). [Link]
    • C/C++, OpenCV
  • Predator. [Link]
    • Matlab.
  • Object Tracking via Partial Least Squares Analysis. [Link]
    • Matlab.
  • Robust Object Tracking via Sparsity-based Collaborative Model. [Link]
    • Matlab.
  • On-line boosting trackers. [Link]
    • C/C++.

Advanced appearance model based trackers

  • Real-Time Compressive Tracking [Link]

Resources

Below is a list with resources including OpenCV documentation, libraries, and OpenCV compatible tools.

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