SentiSight SDK

Object recognition for robotics and computer vision

SentiSight is intended for developers who want to use computer vision-based object recognition in their applications. Through manual or fully automatic object learning it enables searching for learned objects in images from almost any camera, webcam, still picture or live video in an easy, yet versatile, way.

Available as a software development kit that provides for the development of object recognition systems for Microsoft Windows or Linux platforms.

Technical Specifications

All specifications are provided for an Intel Core i7-2600 processor running at 3.4 GHz.

These specifications are for SentiSight 3.4 blob-recognition and shape-recognition algorithms. These algorithms may be used together or separately, depending on object type.

The specifications are provided for 320 x 240 pixel images. These image area performance dependencies are valid for the same images with different resolutions:

  • The blob-based algorithm has linear dependence for object learning and linearithmic (n log n) dependence for object recognition.
  • The shape-based algorithm has linearithmic (n log n) dependence for object learning and quadratic dependence for object recognition.
  • The color usage mode has linear dependence for both object learning and recognition.

The object-model size depends on how feature-rich an object is, thus varying for each object.

These conditions may alter the performance of the algorithms:

  • Rotation and translation. The algorithm is generally rotation- and translation-invariant in a plane perpendicular to the camera. The algorithm is also invariant for rotations of up to 10-15 degrees beyond a plane perpendicular to the camera. Different views of an object may be added to a model to handle larger rotations.
  • Resolution and scale changes. Scale (size of the object within the image) difference between an object's model and the object itself can be up to 2-3 times. Objects should contain enough detail, and be large enough to be recognizable.
  • Occlusions. The algorithm is robust to occlusions as big as 50 % of the object if enough unique edges remain visible.
  • Lighting conditions (illumination, shadows and reflectance).
    • Planar objects will have issues with reflectance.
    • 3D objects will have issues with variable lighting conditions – consistent lighting greatly reduces potential problems.
  • Transparency. In general, transparent objects may be difficult to recognize.
  • Rigidity. The algorithm recognizes only rigid/stable objects. At minimum, a significant piece of the object should be unchanging.

Object recognition algorithms may be configured to run on more than one thread of multi-core processors, allowing for an increase in object-model matching speed. The table below provides object recognition speeds as a range; the smaller number represents recognition speed using 1 thread, while the larger number represents recognition speed using 8 threads. Note that the specified processor model has 4 cores and executes 2 threads per processor core in parallel.

SentiSight 3.4 blob-based object recognition algorithm - technical specifications
  Without color
usage mode
With color
usage mode
Static Background Extraction/
Object mask separation
30 frames per second
Learning: Processing of single object's frame 0.014 seconds 0.017 seconds
Learning: Generalization time
(for 100 frames of object)
0.15 seconds
Recognition speed (1) 160,000 - 290,000
models per second
90,000 - 140,000
models per second

SentiSight 3.4 shape based object recognition algorithm - technical specifications
Static Background Extraction/
Object mask separation
30 frames per second
Learning: Processing of single object's frame 0.215 seconds
Learning: Generalization time
(for 100 frames of object)
Not applicable
Recognition speed (1) 3,500 - 8,000 models per second

(1) When object model contains one template. The object model may contain multiple templates (usually corresponding with different viewpoints). In that case the algorithm will compare an object against all templates in the model before returning the recognition result. Also, this recognition speed is reached with sufficiently large databases (2,000 images or more); with smaller databases the recognition is slower.

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