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Title:Prepoznavanje aktivnosti osebe iz zaporedja slik z globokimi povratnimi nevronskimi mrežami : diplomsko delo
Authors:Pintarič, David (Author)
Potočnik, Božidar (Mentor) More about this mentor... New window
Šavc, Martin (Co-mentor)
Files:.pdf UN_Pintaric_David_2019.pdf (3,78 MB)
MD5: 105ED7C777B9ADABCC234A1791B0438A
 
Language:Slovenian
Work type:Bachelor thesis/paper (mb11)
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomskem delu se ukvarjamo s problemom prepoznavanja aktivnosti osebe iz zaporedja slik, pri čemer prepoznavo poskušamo izboljšati z upoštevanjem časovne komponente. To dosežemo z uporabo povratnih nevronskih mrež. Omejili smo se na naslednje aktivnosti: oseba ni v ravnovesju, se pripogiba, stoji, sedi, leži, hitro hodi, počasi hodi in pada. Pregledali smo obstoječe postopke prepoznavanja, preučili povratne nevronske mreže, pripravili množico podatkov, zasnovali algoritem, izvedli eksperimente in na koncu analizirali rezultate. Rezultati na 25 označenih videoposnetkih so pri uporabi povratne nevronske mreže pokazali 83,24 % povprečno natančnost pri uporabi tipa zaporedje v vektor in 75,53 % povprečno natančnost pri uporabi tipa zaporedje v zaporedje. Kljub temu da so dobljeni rezultati boljši od tistih, kjer ne upoštevamo časovne komponente, ugotavljamo, da povratne nevronske mreže zaradi računske zahtevnosti niso vedno najboljša izbira.
Keywords:računalniški vid, povratna nevronska mreža, pomnilna celica LSTM, pomnilna celica GRU, globoko učenje, detekcija oseb, prepoznavanje aktivnosti osebe
Year of publishing:2019
Place of performance:Maribor
Publisher:[D. Pintarič]
Number of pages:VII, 46 str.
Source:Maribor
UDC:004.8:004.93(043.2)
COBISS_ID:22912790 New window
NUK URN:URN:SI:UM:DK:ULUXJAAG
Views:514
Downloads:151
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:11.09.2019

Secondary language

Language:English
Title:Person activity recognition from image sequence using deep recurrent neural networks
Abstract:The diploma thesis deals with the problem of person activity recognition from a sequence of images, while trying to improve recognition by taking into account the temporal data component. This is achieved through the use of recurrent neural networks. The focus was limited to the following activities: a person is out of balance, bending, standing, sitting, lying down, walking fast, walking slowly and falling. The existing identification methods were reviewed, the recurrent neural networks were examined, a large dataset was prepared, an algorithm was designed, experiments were conducted and finally the results were analysed. The results on the 25 labeled videos showed an 83.24% average accuracy rate when using a sequence-to-vector type recurrent neural network and a 75.53% average accuracy rate when using a sequence-to-sequence type of a recurrent neural network. Although the results obtained are better than those where the temporal data component is disregarded, it can be concluded that recurrent neural networks, due to the computational complexity, are not always the best choice.
Keywords:computer vision, recurrent neural network, LSTM cell, GRU cell, deep learning, human object recognition, human activity recognition, person activity recognition


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