Small moving object detection using adaptive subband decomposition and fractional lower order statistics in video sequences

Abstract

Abstract. In this paper, a small moving object method detection method in video sequences is described. In the first step, the camera motion is eliminated using motion compensation. An adaptive subband decomposition structure is then used to analyze the motion compensated image. In the highband subimages moving objects appear as outliers and they are detected using a statistical detection test based on lower order statistics. It turns out that in general, the distribution of the residual error image pixels is almost Gaussian. On the other hand, the distribution of the pixels in the residual image deviates from Gaussianity in the existence of outliers. By detecting the regions containing outliers the boundaries of the moving objects are estimated. Simulation examples are presented.

Shadow detection using 2D cepstrum

“…Background subtraction is commonly used for segmenting out moving objects in a scene for surveillance applications. There are several methods in the literature [1][2][3] for moving object detection in video. The background estimation algorithm described in [1] uses a simple IIR filter applied to each pixel independently to update the background and use adaptively updated thresholds to classify pixels into foreground and background.…”

“…Assuming Eq. (6) holds, second and third 2D cepstral coefficients x_im^ [2] and x_im^ [3] corresponding to x_im and their counterparts x_bg^ [2] and x_bg^ [3] corresponding to x_bg should be the same. This can ideally be represented as:…”

“…Other more sophisticated methods, including the ones developed by Bagci et al [2] and Stauffer and Grimson [3], can also be used for moving pixel estimation. In our application, accurate detection of moving regions is not as critical as in other object tracking and estimation problems; we are mainly concerned with real-time detection of moving shadows as part of shadow elimination step of a computer vision based forest fire detection system.…”

“…Moving shadow and shaded regions are major sources of false alarms for most of the moving object detection and tracking algorithms in video [1][2][3]. These algorithms basically depend on tracking the change in the intensity values of pixels over time.…”

Real-Time Fire and Flame Detection in Video

“…Moving regions are determined by connected component analysis. Other methods like [7] and [8] can also be used for moving pixel estimation.…”