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Pregled bibliografske jedinice broj: 936222

Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach


Presečan, Mihael
Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach, 2017., diplomski rad, diplomski, Fakultet elektrotehnike i računarstva, Zagreb


CROSBI ID: 936222 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach

Autori
Presečan, Mihael

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad, diplomski

Fakultet
Fakultet elektrotehnike i računarstva

Mjesto
Zagreb

Datum
17.07

Godina
2017

Stranica
54

Mentor
Kovačić, Zdenko

Neposredni voditelj
Petric, Frano

Ključne riječi
deep learning, object detection, object classification, Faster R-CNN, ADORE, ADOS, ADOSet, free-play, autism

Sažetak
This research sets the ground for the new task in ADORE protocol - the free play observation. The core problem to solve is to develop accurate, robust and fast object detector. This research have proposed Deep Learning approach for object detection and classification for the child behavior observation task. The task is used in the context of autism diagnostics, which uses the ADOS protocol with it’s standardized toys. Child behavior has been determined by it’s playing with the toys, and in order to detect the toys and the child, a new dataset has been developed. Also, this research had developed algorithms and mechanism to determine child’s attention based on the toys that child is playing with. This thesis disscuses the challenges encountered in the research and their solutions, and as well sets the work for continuing the development of robust and accurate attention analyzer.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Edukacijsko-rehabilitacijske znanosti



POVEZANOST RADA


Projekti:
HRZZ-IP-2013-11-1024 - Dijagnostika autizma s robotskim evaluatorom (ADORE) (Kovačić, Zdenko, HRZZ ) ( CroRIS)

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Zdenko Kovačić (mentor)

Avatar Url Frano Petric (mentor)


Citiraj ovu publikaciju:

Presečan, Mihael
Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach, 2017., diplomski rad, diplomski, Fakultet elektrotehnike i računarstva, Zagreb
Presečan, M. (2017) 'Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach', diplomski rad, diplomski, Fakultet elektrotehnike i računarstva, Zagreb.
@phdthesis{phdthesis, author = {Prese\v{c}an, Mihael}, year = {2017}, pages = {54}, keywords = {deep learning, object detection, object classification, Faster R-CNN, ADORE, ADOS, ADOSet, free-play, autism}, title = {Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach}, keyword = {deep learning, object detection, object classification, Faster R-CNN, ADORE, ADOS, ADOSet, free-play, autism}, publisherplace = {Zagreb} }
@phdthesis{phdthesis, author = {Prese\v{c}an, Mihael}, year = {2017}, pages = {54}, keywords = {deep learning, object detection, object classification, Faster R-CNN, ADORE, ADOS, ADOSet, free-play, autism}, title = {Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach}, keyword = {deep learning, object detection, object classification, Faster R-CNN, ADORE, ADOS, ADOSet, free-play, autism}, publisherplace = {Zagreb} }




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