Thesis (MSc)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: Object detection and tracking are important components of many computer
vision applications including automated surveillance. Automated surveillance
attempts to solve the challenges associated with closed-circuit camera systems.
These include monitoring large numbers of cameras and the associated
labour costs, and issues related to targeted surveillance. Object detection is
an important step of a surveillance system and must overcome challenges such
as changes in object appearance and illumination, dynamic background objects
like ickering screens, and shadows. Our system uses Gaussian mixture
models, which is a background subtraction method, to detect moving objects.
Tracking is challenging because measurements from the object detection stage
are not labelled and could be from false targets. We use multiple hypothesis
tracking to solve this measurement origin problem. Practical long-term tracking
of objects should have re-identi cation capabilities to deal with challenges
arising from tracking failure and occlusions. In our system each tracked object
is assigned a one-class support vector machine (OCSVM) which learns the
appearance of that object. The OCSVM is trained online using HSV colour
features. Therefore, objects that were occluded or left the scene can be reidenti
ed and their tracks extended. Standard, publicly available data sets are
used for testing. The performance of the system is measured against ground
truth using the Jaccard similarity index, the track length and the normalized
mean square error. We nd that the system performs well. / AFRIKAANSE OPSOMMING: Die opsporing en volging van voorwerpe is belangrike komponente van baie
rekenaarvisie toepassings, insluitend outomatiese bewaking. Outomatiese bewaking
poog om die uitdagings wat verband hou met geslote kring kamera
stelsels op te los. Dit sluit in die monitering van groot hoeveelhede kameras en
die gepaardgaande arbeidskoste, en kwessies wat verband hou met toegespitse
bewaking. Die opsporing van voorwerpe is 'n belangrike stap in 'n bewakingstelsel
en moet uitdagings soos veranderinge in die voorwerp se voorkoms en
beligting, dinamiese agtergrondvoorwerpe soos ikkerende skerms, en skaduwees
oorkom. Ons stelsel maak gebruik van Gaussiese mengselmodelle, wat
'n agtergrond-aftrek metode is, om bewegende voorwerpe op te spoor. Volging
is 'n uitdaging, want afmetings van die voorwerp-opsporing stadium is
nie gemerk nie en kan afkomstig wees van valse teikens. Ons gebruik verskeie
hipotese volging (multiple hypothesis tracking ) om hierdie meting-oorsprong
probleem op te los. Praktiese langtermynvolging van voorwerpe moet heridenti
seringsvermoëns besit, om die uitdagings wat voortspruit uit mislukte
volging en okklusies te kan hanteer. In ons stelsel word elke gevolgde voorwerp
'n een-klas ondersteuningsvektormasjien (one-class support vector machine,
OCSVM) toegeken, wat die voorkoms van daardie voorwerp leer. Die OCSVM
word aanlyn afgerig met die gebruik van HSV kleurkenmerke. Daarom kan
voorwerpe wat verdwyn later her-identi seer word en hul spore kan verleng
word. Standaard, openbaar-beskikbare datastelle word vir toetse gebruik. Die
prestasie van die stelsel word gemeet teen korrekte afvoer, met behulp van die
Jaccard ooreenkoms-indeks, die spoorlengte en die genormaliseerde gemiddelde
kwadraatfout. Ons vind dat die stelsel goed presteer.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/86632 |
| Date | 04 1900 |
| Creators | Keaikitse, Advice Seiphemo |
| Contributors | Brink, W., Govender, N., Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | 94 p. : ill. |
| Rights | Stellenbosch University |
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