We introduce a new approach for salient motion in dynamic scenes. The proposed method is based on dynamic
texture (DT) model and observability of the linear systems. Each video clip of τ frames is represented by DT in a holistic
manner and the learned DT parameters are used to form an efficient formula of measuring observability. The formula is
related to time-domain eigenvalues and eigenvectors. But the eigenvalue decomposition operation is not needed. The
salient motion is detected by thresholding the observability values of each pixel location. Besides, we also introduce the
system property of observability which can be used to reduce computational cost. Our method is tested on a challenging
sequences set. Experimental results show that the proposed method has better trade-off between detection results and
time efficiency than most current methods.
Detecting moving objects is the first step of many video surveillance applications. Most existing simple background
subtraction methods such as frame difference, running average (RA), and median filter, have the low computational cost,
but they can't perform well in the complex scenes. Although some ordinary methods can do well in the complex scenes,
they can't satisfy the real-time requirement because of its high computational cost. So in this paper, we propose an
efficient approach for detecting moving objects, which has the low computational cost and high performance in the
complex scenes. The proposed method first uses the running average algorithm and contour information to obtain
moving regions roughly. Then an improved GMM algorithm is used to update the background model and detect
foreground precisely. The experiment results show that our method has a lower computational cost and performs better
both in the outdoor and indoor scenes than GMM.
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