Background subtraction and object tracking with applications in visual surveillance

Ka Ki Ng, Purdue University

Abstract

Visual surveillance has been a very active research topic in the last few years due to its growing importance in security, law enforcement, and military applications. More and more surveillance cameras are installed in security sensitive areas such as banks, train stations, highways, and borders. The massive amount of data involved makes it infeasible to guarantee vigilant monitoring by human operators for long periods of time due to monotony and fatigue. As a result, video feeds are usually archived for forensic purposes in the event suspicious activities take place. In order to assist human operators with identification of important events in videos an “intelligent” visual surveillance system can be used. Such a system requires fast and robust methods for moving object detection, tracking, and event analysis. In this thesis, we investigate methods for moving object detection, tracking, and event analysis. We consider robustness and computational cost as the major design goals of our work. Our proposed method detects moving objects in indoor and outdoor environments under changing illumination conditions and in the presence of background dynamics. We also present a fast implementation of the method using an extension of integral images. We propose a method for robust tracking with dynamic parameter setting for the likelihood model of particle filtering. In addition, we propose a fast method to construct an appearance model for object tracking using a particle filtering framework. We also present a method for pedestrian “flow” estimation that counts the number of persons passing a detection line (trip wire) or a designated region over a period of time. The method is based on accumulated foreground pixel count in the trip wire and texture features in an area enclosing the trip wire. The method is designed to be robust to varying pedestrian flow rate (crowdedness).

Degree

Ph.D.

Advisors

Delp, Purdue University.

Subject Area

Electrical engineering

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