Advertisement
Published in:
2017 | OriginalPaper | Chapter
Semi-automatic Training of an Object Recognition System in Scene Camera Data Using Gaze Tracking and Accelerometers
Authors : Matteo Cognolato, Mara Graziani, Francesca Giordaniello, Gianluca Saetta, Franco Bassetto, Peter Brugger, Barbara Caputo, Henning Müller, Manfredo Atzori
Published in: Computer Vision Systems
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
Object detection and recognition algorithms usually require large, annotated training sets. The creation of such datasets requires expensive manual annotation. Eye tracking can help in the annotation procedure. Humans use vision constantly to explore the environment and plan motor actions, such as grasping an object.
In this paper we investigate the possibility to semi-automatically train object recognition with eye tracking, accelerometer in scene camera data, learning from the natural hand-eye coordination of humans. Our approach involves three steps. First, sensor data are recorded using eye tracking glasses that are used in combination with accelerometers and surface electromyography that are usually applied when controlling prosthetic hands. Second, a set of patches are extracted automatically from the scene camera data while grasping an object. Third, a convolutional neural network is trained and tested using the extracted patches.
Results show that the parameters of eye-hand coordination can be used to train an object recognition system semi-automatically. These can be exploited with proper sensors to fine-tune a convolutional neural network for object detection and recognition. This approach opens interesting options to train computer vision and multi-modal data integration systems and lays the foundations for future applications in robotics. In particular, this work targets the improvement of prosthetic hands by recognizing the objects that a person may wish to use. However, the approach can easily be generalized.