SSTT - Augmented Reality Tracking Library

SSTT - (Spezialized Spatial Target Tracker) is a computer vision based tracking framework for Augmented and Mixed Reality applications. SSTT aims to be used for creating special tracking systems that solve tracking issues that are not solvable with existing tools.

However, SSTT also provides some generic methods:

  • fiducial markers with image-ids (similar to ARToolKit)
  • fiducial markers with ECC based codes (similar to ARTag or ARUCO)
  • image based markers (similar to Vuforia, ARcore and ARKit)
  • model based, multiplanar markers
  • any combination of above


The SSTT software framework is highly portable and runs on a number of devices including mobile phones. It is optimized for speed and is modular in terms of the concepts used to track targets. SSTT goal is focuses on robustness over speed but is also faster than other libraries. SSTT can utilize various SIMD instruction through integrating ORC.

In a full rewrite SSTT is now (with Version 4.x), independent of OpenCV and using modern C++23 concepts. Further development is needed to regain feature parity with SSTT 3.x.




SSTT Edglets

SSTT Fourier Tags


SSTT has the following features

  • various tracking methods for model based, rectangular, natural feature and shape tracking targets
  • tracking sub-modules for skin and face recognition
  • high performance even on large input video streams (e.g. HD1080p)
  • numerous filtering methods for detection and pose estimation
  • uses a spatial statistical model to optimize visibility information
  • 6DOF pose estimation optimized for usage with OpenGL
  • dedicated video capture modules to reduce the time spent for frame retrieval

Supported Platforms

SSTT core is highly portable and comes with a highly flexible CMake build system and a Catch2 based test-harness.

Capture backends

  • GStreamer 0.10 (Linux, Mac, Windows, Maemo)
  • V4L2 (Linux, Maemo)
  • WMF (Windows 7 upwards)
  • DirectShow (Windows/Windows Mobile)
  • QTKit / CoreVideo (Mac)
  • QTMovie (Mac)
  • AVCaptureSession (iOS, MacOS)
  • CameraPreviewCallback (Android)
  • IDS uEye Camera
  • Pipewire (Linux WIP)

Collaboration and Licensing

SSTT is a research tool and under constant development. Therefore, I welcome any requests for research collaborations from academia and industry on topics covered in SSTT, namely natural feature tracking, Structure from Motion, Around Device Interaction etc. pp.


Since 2023 SSTT is invite-only available under the terms of the MIT License.

Hartmut Seichter
Hartmut Seichter
Professor of Computer Graphics

My research interests are Augmented and Virtual Reality in combination with Tangible User Interfaces.