A novel deep neural network that uses space-time features for tracking and recognizing a moving object

• Autorzy: Chang O. , Constante P. , Gordon A. , Singaña M.

Identyfikatory

DOI

10.1515/jaiscr-2017-0009

• Języki publikacji: EN

Abstrakty

EN

This work proposes a deep neural net (DNN) that accomplishes the reliable visual recognition of a chosen object captured with a webcam and moving in a 3D space. Autoencoding and substitutional reality are used to train a shallow net until it achieves zero tracking error in a discrete ambient. This trained individual is set to work in a real world closed loop system where images coming from a webcam produce displacement information for a moving region of interest (ROI) inside the own image. This loop gives rise to an emergent tracking behavior which creates a self-maintain flow of compressed space-time data. Next, short term memory elements are set to play a key role by creating new representations in terms of a space-time matrix. The obtained representations are delivery as input to a second shallow network which acts as ”recognizer”. A noise balanced learning method is used to fast train the recognizer with real-world images, giving rise to a simple and yet powerful robotic eye, with a slender neural processor that vigorously tracks and recognizes the chosen object. The system has been tested with real images in real time.

Słowa kluczowe

EN

deep architectures; deep learning; artificial vision

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2017
• Tom: Vol. 7, No. 2
• Strony: 125--136
• Opis fizyczny: Bibliogr. 16 poz., rys.

Twórcy

autor

Chang O.

• Department of Software Innovation, Farmaenlace, Quito Ecuador

autor

Constante P.

• Department of Energy and Mechanics, Universidad de las Fuerzas Armadas ESPE Latacunga Ecuador

autor

Gordon A.

• Department of Energy and Mechanics, Universidad de las Fuerzas Armadas ESPE Latacunga Ecuador

autor

Singaña M.

• Department of Energy and Mechanics, Universidad de las Fuerzas Armadas ESPE Latacunga Ecuador

Bibliografia

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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).