出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Object tracking can be defined as the process of detecting an object of interest from a videoscene and keeping track of its motion, orientation, occlusion etc. in order to extract usefulinformation. It is indeed a challenging problem and it’s an important task. Many researchersare getting attracted in the field of computer vision, specifically the field of object tracking invideo surveillance. The main purpose of this paper is to give to the reader information of thepresent state of the art object tracking, together with presenting steps involved in BackgroundSubtraction and their techniques. In related literature we found three main methods of objecttracking: the first method is the optical flow; the second is related to the backgroundsubtraction, which is divided into two types presented in this paper, and the last one is temporaldifferencing. We present a novel approach to background subtraction that compare a currentframe with the background model that we have set before, so we can classified each pixel of theimage as a foreground or a background element, then comes the tracking step to present ourobject of interest, which is a person, by his centroid. The tracking step is divided into twodifferent methods, the surface method and the K-NN method, both are explained in the paper.Our proposed method is implemented and evaluated using CAVIAR database.
关键词：Video Surveillance; Object Tracking; Feature Extraction; Background Subtraction; Big data
