IVA for abandoned object detection: Exploiting dependence across color channels

Abstract

Automated detection of abandoned object (AO) is an important application in video surveillance for security purposes. Because of its importance, a number of techniques have been proposed to automatically detect abandoned objects in the past years. However, these techniques require prior knowledge on the properties of the object such as its shape and color, in order to classify foreground objects as abandoned object. In contrast, independent component analysis (ICA) does not require such prior knowledge. However, it can only model one dataset at a time, thus limiting its usage to monochrome frames. In this paper, we propose to use independent vector analysis (IVA), a recent extension of ICA to multivariate data that takes the dependence across multiple datasets into account while retaining the independence within each dataset. We present a new framework for AO detection using IVA and show that it provides successful performance in complicated scenarios, such as for videos with crowd, illumination change, and occlusion.