ENEE MS Thesis Defense
Blind source separation for detection of abandoned objects:
Exploiting different types of diversity
Suchita Bhinge
2:30pm Friday, 13 November 2015, ITE 325B
Due to the increase in security concerns, automated detection of abandoned objects has become an important application in video surveillance. Because of its increasing importance, a number of techniques have been proposed recently to automatically detect abandoned objects. The general procedure implemented for detection of abandoned objects includes background subtraction or foreground object extraction followed by post-processing steps in order to classify the foreground object as an abandoned or non-abandoned object. However, these techniques make use of a number of user-defined parameters such as track time, co-ordinates of the object/owner, the vicinity of the object, and properties of the object such as its shape, color, among others.
In this thesis, we present a new technique based on blind source separation (BSS) for detection of abandoned objects that does not keep track of the extracted objects or owners and does not require a dual background scheme for stationary object extraction. Order selection is an important step for our implementation of blind source separation based scheme since this step captures the signals with high energy and disregards signals that are not relevant to the detection of abandoned objects. In this thesis, we show that the performance of ICA improves when an algorithm that assumes a flexible source distribution along with multiple types of diversity, such as higher-order statistics and sample dependence is used for the estimation of the source components. ICA, however, can only model one dataset at a time, thus limiting its usage to monochrome frames. In order to address this issue, we also present another implementation of blind source separation called independent vector analysis (IVA), a recent extension of ICA to multiple data that takes the dependence across multiple datasets into account while retaining the model of independent components within each dataset. We show that the proposed blind source separation techniques performs successfully in complicated scenarios such as crowd, occlusion, and illumination changes.
Committee: Drs. Tulay Adali (chair), Joel Morris and Mohamed Younis