Type
ThesisAuthors
Wang, Su
Advisors
Zhang, Xiangliang
Committee members
Gao, Xin
Moshkov, Mikhail

Program
Computer ScienceDate
2016-05-23Embargo End Date
2017-05-23Permanent link to this record
http://hdl.handle.net/10754/610580
Metadata
Show full item recordAccess Restrictions
At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2017-05-23.Abstract
We trained a combined deep convolutional neural network to predict seals’ age (3 categories) and gender (2 categories). The entire dataset contains 110 seals with around 489 thousand location records. Most records are continuous and measured in a certain step. We created five convolutional layers for feature representation and established two fully connected structure as age’s and gender’s classifier, respectively. Each classifier consists of three fully connected layers. Treating seals’ latitude and longitude as input, entire deep learning network, which includes 780,000 neurons and 2,097,000 parameters, can reach to 70.72% accuracy rate for predicting seals’ age and simultaneously achieve 79.95% for gender estimation.Citation
Wang, S. (2016). Exploring Ocean Animal Trajectory Pattern via Deep Learning. KAUST Research Repository. https://doi.org/10.25781/KAUST-29M51ae974a485f413a2113503eed53cd6c53
10.25781/KAUST-29M51