Long-term tracking of multiple interacting pedestrians using a single camera

Keaikitse, Advice Seiphemo (2014-04)

Thesis (MSc)--Stellenbosch University, 2014.

Thesis

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.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/86632
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