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Object recognition - SentiSight SDK

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. SentiSight is available as a software development of object recognition systems for Microsoft Windows or Linux platforms.

  • Description
  • Advantages
  • System Requirements
  • Video

SentiSight has two operation modes: learning and recognition. In learning mode, the SentiSight algorithm creates an object model by extracking object features from an image or video. In recognition mode SentiSight finds and tracks objects with features matching those previously stored in objct models.

Object learning process

SentiSight supports 2 methods of object learning: manual and automatic.

Manual object learning is suitable for most situations. A user performs the following steps for manual object learning in the SentiSight-based application:

  1. Outline an object's shape on an image by marking the object'wcorner points to build a polygon. The image can be provided from an image file, video file or live stream.
  2. Select the algorithm to use: blob-based, shape-based, or both.
  3. As an option, additional images of the object may be provided, repeating Step 1 for each image. The algorithm assists the user by estimating an approximate shape for the object if the image is recognized by way of previously catalogued images. Learning the object from different sides and angles results in better recognition quality.
  4. Input the learned object name (ID) into the system.


Automatic object learning is suitable for lightweight, movable objects. This learning procedure is based on detecting an object through the exclusion of static background and the object's holder (usually a hand). A fixed camera is highly recommended for this process. A user performs the following steps for automatic object learning in the SentiSight-based application:

  1. Select background and position the camera.
  2. Select a holder - an object that will be used to hold and move the object to be learned. A user's hand can be the "holder".
  3. The "holder" if it is not a rigid object should be presented to the camera in various poses and configurations so SentiSight can learn it.
  4. Select the algorithm to use: blob-based, shape-based, or both.
  5. After the holder has been learned, SentiSight is ready to learn the object itself. Use the holder to rotate and move the object, both closer to and further from the camera.
  6. Input the learned object name (ID) into the system.


Reliability Tests

All tests were performed on an Intel Core i7-2600 processor running at 3.4 GHz.

The SentiSight3.3 algorithm was tested with a subset of Amsterdam Library of Object Images (ALOI)

  • The subset contained objects 1-100 from ALOI.
  • Images with object viewpoint variations (ALOI-VIEW collection) were used 36 images per object were used.


The blob- and shape-based algorithms from SentiSight 3.3 were tested separately. SentiSight 3.3 performance was tested on these image resolutions:


  • 768 x 576 pixels - the original full resolution images from ALOI.
  • 320 X 240 pixels - obtained by resizing the 768 x 576 images before testing.

At 0.1% False Acceptance Rate (FAR), the recognition rate is from 70%-99%, depending on object structural appearance, transparency, etc. For objects with well-defined internal structure, the recognition rate is 98%-99% at 0.1% FAR.




Suitable for

  • Banks
  • State
  • Business
  • Stores
  • Hotels


Advantages of SentiSight SDK

  • Reliable, innovative algorithm that is tolerant of variation in appearance, object scale, rotation and pose.
  • Accurate detection, processing and tracking of objects in real-time.
  • Webcams or other low cost cameras are suitable for obtaining object images.
  • Available as a multiplatform SDK that supports multiple programming languages.


System Requirements

PC with x86 (32-bit) or x86-64 (64-bit) processor:

  • SSE2 support is required. Processors that do not support SSE2 cannot run the SentiSight 3.3 algorithm. Please check if  particular processor model supports SSE2 instruction set.
  • SSSE3 support is recommended, as the SentiSight 3.3 algorithm provides higher performance using this instruction set. 
  • 64-bit architecture allows to work with large images, larger model databases and also increases general SentiSight 3.3 algorithm performance due to usage of 64-bit CPU registers.

At least 256 MB of free RAM should be available for the SentiSight-based application. Additional RAM may be required for:

  • Applications that need to recognize objects using large database as the whole database must be loaded into RAM before recognition. The database size depends on objects quantity and nymber of templates saved in each object model. Each object model may be rather large due to using long videos for learning (as template learned from each frame is saved separately) and/or using multiple views for each object. For example, a database of 100 object modelswith 36 templates per model will require about 25 MB of RAM when blob recognition algorithms used, or about 50 MB when shape recognition algorithms is used (for 320 x 240 pixels resolution).
  • Applications that need to work with high resolution videos. Higher resolution allows to extract more features from objects, thus the object model sizes will be bigger. In general the template size has about linear dependence from the image or video resolution. 

Optional camera or webcam. These cameras are supported by SentiSight:

  • Any webcam or camera that is accessible using:
    • DirectShow interface for Microsoft Windows platform.
    • GStreamer interface for Linux platform.
  • Any IP camera, that supports RTSP (Real Time Streaming Protocol):
    • Only RTP over UDP is supported.
    • H.264/MPEG-4 AVC or Motion JPEG should be used for encoding the video stream.
  • These models of still cameras are supported:
    • Cannon EOS family still cameras (Microsoft Windows only).
    • Nikon DSLR still cameras (Microsoft Windows only, a specific camera model should support video capture and should be listed there)

Microsoft Windows specific requirements:

  • Microsoft Windows XP/Vista/7/8/Server 2003/Server 2008/Server 2008 R2/ Server 2012, 32-bit or 64-bit.
  • Microsoft.NET framework 3.5 or newer (for .NET components usage).
  • MicrosoftDirectX 9.0 or later (for camera/webcam usage).
  • One of the following development environments for application development:
    • Microsoft Vissual Studio 2008 SP1 or newer (for application development under C/C++, C#, Visual Basic.Net).
    • Sun Java 1.6 SDK or later.




See the video here


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