A real-time background subtraction method with camera motion compensation

0-7803-8603-5/04/$20.00 IEEE 02004
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2. The proposed method
Figure I illustrates the block diagram of the algorithm. The input framc is compensated and compared with the given background frame to extract foreground objects. Motion estimation is known to he computationally expensive. In order to perform background elimination including motion analvsis in real time, we need to design each phase in the algorithm carefully.
2004 IEEE International Conference on Multimedia and Expo (ICME)
A Real-Time Background Subtraction Method with Camera Motion Compensation
Tiehan L v Department of Electrical Engineering, Princeton University, Princeton, NJ 08544 Iv@
ABSTRACT
Background subtraction algorithms are critical to many video recognitiodanalysis systems and have been studied for decades. Most of the algorithms assume that the camera is fixed. In this paper, we propose a background subtraction algorithm that works when a shaking camera is present. In this algorithm, the input frames are compensated and compared with the given reference frame to separate foreground objects from the background. The experimental results show that the proposed method outperforms the widely used Gaussian mixture model based method in both f i e d camera and shaking camera scenarios with respect to accuracy, robustness, and eficiency.
blocks with significant changes. Yang et a1 [7]proposed an edge-based method, which compares the edge information of the current Frame with the background edge information to determine the introduced objects. The majority of the background subtraction algorithms fall into the pixel based category. The simplest method is to subtract the stored background image from the input frame and use thresbolding to determine the foreground pixels. However, a slight lighting condition change or aperture adjustment performed by the camera will result false positive pixels. For this reason, researchers have already moved to adaptive background subtraction methods. Wren et al [2] developed an adaptive Gaussian model in their Pfinder system. Ridder et al [I] used Kalman filtering for background adaptation. The movement of trees and bushes in outdoor environments can cause serious problems for many background subtraction algorithms. To solve this problem, Gaussian mixture models have been used in several video surveillance applications [3]. Rittscher et al [SI developed a three state HMM based background subtraction algorithm for a traffic monitoring system where the states represent background, shadow, and foreground. Most of these methods are focusing on fixed camera systems. However, camera motion is inevitable for many systems, e.g. cameras fixed in a moving vehicle or on a bridge. Several tracking algorithms with active cameras are reported in literature [9], [IO]. These algorithms can successfully track interested objects under various conditions. However, most of them may fail to identify slow-moving objects or partial-moving objects such as a standing person with waving bands. The rest of this paper is organized as follows. Section 2 presents our proposed algorithm. Section 3 gives experimental results and compares our algorithm with Gaussian mixture method. Section 4 concludes the paper and covers future work.
1. Introduction
In most real-time applications, background subtraction has been widely used for its simplicity and effectiveness. Although research on background subtraction has been continued for decades, it is still an open problem since most of the algorithms still work under very restricted conditions. In this paper, we present a camera motion compensated background subtraction method that works robustly with shaking camera. Our method incorporates robust and efficient motion estimation and compensation schemes to combat camera resulted motion. In addition, we replace the widely used morphology operations by adopting a spatial filter to suppress various noises. This modification reduces computational complexity of our algorithm and improves its robustness over the noise resulted by trees. Many factors make background subtraction a difficult task, such as lighting condition change, shadow, reflection, and varying background. Most of the background subtraction algorithms in the literature can be classified into three categories, pixel based, edge based, and block based. Hsu et al [6] proposed a block-based approach, which uses statistical likelihood test to determine the