Browsing by Author "Keaikitse, Advice Seiphemo"

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    Long-term tracking of multiple interacting pedestrians using a single camera
    (Stellenbosch : Stellenbosch University, 2014-04) Keaikitse, Advice Seiphemo; Brink, Willie; Govender, N.; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences.
    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.