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A Neural architecture for recognising human actions in video sequences
Thesis (MEng)--Stellenbosch University, 2022.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2022
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| _version_ | 1867614112246136832 |
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| access_status_str | Open Access |
| author | Malan, Christian |
| author2 | Du Preez, Johan |
| author_browse | Du Preez, Johan Malan, Christian |
| author_facet | Du Preez, Johan Malan, Christian |
| author_sort | Malan, Christian |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2022. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/124921 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:46:51.765Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/124921 A Neural architecture for recognising human actions in video sequences Malan, Christian Du Preez, Johan Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. UCTD Neural network computers Artificial intelligence Action recognition model Thesis (MEng)--Stellenbosch University, 2022. ENGLISH ABSTRACT: The goal of an action recognition model (as applied to a video) is to extract distinct features that accurately characterise the action present and then from that determine the dominant action in the video. Since such actions occur in the spatio-temporal domain, recognising it relies on both spatial and temporal features. In this thesis we designed and built such an action recognition model by making use of a deep neural network system that combines various subsystems, each focussing on complemen tary aspects of the problem. A particular focus is on the automatic extraction of discriminatory spatio-temporal features. In combination the final system is shown to accurately distinguish between a limited range of actions. AFRIKAANSE OPSOMMING: Die doel van ’n aksieherkenningsmodel (soos toegepas op ’n video) is om eerstens duidelike kenmerke te onttrek wat die aksie akkuraat beskryf, en dan daaruit die dominante aksie teenwoordig in die video te bepaal. Aangesien sulke aksies in beide tyd en ruimte plaasvind, moet ’n herkenner beide van hierdie domeine in ag neem. In hierdie tesis het ons ’n diep neuralenetwerk stelsel vir hierdie doel ontwikkel. Dit maak gebruik van verskeie substelsels wat elk fokus op komplementêre aspekte van die probleem. ’n Besondere fokus is op die outomatiese onttrekking van diskriminerende tydruimtelike kenmerke. In kombinasie is die finale stelsel in staat om ’n beperkte reeks aksies akkuraat te onderskei. Masters 2022-03-02T10:22:38Z 2022-04-29T09:41:15Z 2022-03-02T10:22:38Z 2022-04-29T09:41:15Z 2022-04 Thesis http://hdl.handle.net/10019.1/124921 en_ZA Stellenbosch University 135 pages application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | UCTD Neural network computers Artificial intelligence Action recognition model Malan, Christian A Neural architecture for recognising human actions in video sequences |
| title | A Neural architecture for recognising human actions in video sequences |
| title_full | A Neural architecture for recognising human actions in video sequences |
| title_fullStr | A Neural architecture for recognising human actions in video sequences |
| title_full_unstemmed | A Neural architecture for recognising human actions in video sequences |
| title_short | A Neural architecture for recognising human actions in video sequences |
| title_sort | neural architecture for recognising human actions in video sequences |
| topic | UCTD Neural network computers Artificial intelligence Action recognition model |
| url | http://hdl.handle.net/10019.1/124921 |
| work_keys_str_mv | AT malanchristian aneuralarchitectureforrecognisinghumanactionsinvideosequences AT malanchristian neuralarchitectureforrecognisinghumanactionsinvideosequences |