Moving Region Detection

Moving pixels and regions in the video are determined by using a hybrid background estimation method developed by Collins et al. [6]. Let $x_n[k,l]$ represent the intensity (brightness) value at pixel position $[k,l]$ in the $n^{th}$ image frame $x_n$. Estimated background intensity value at the same pixel position, $B_{n+1}[k,l]$, is calculated as follows:

$$B_{n+1}[k,l] = \left\{ \begin{array}{ll} aB_n[k,l]+(1-a)x_n[... ...[k,l] & \textrm{if $[k,l]$\ is moving,}\\ \end{array} \right.$$ (1)

where $B_n[k,l]$ is the previous estimate of the background intensity value at the same pixel position. The update parameter $a$ is a positive real number close to one. Initially, $B_0[k,l]$ is set to the first image frame $x_0[k,l]$. A pixel positioned at $[k,l]$ is assumed to be moving if the brightness values corresponding to it in image frame $x_n$ and image frame $x_{n-1}$ satisfy the following inequality:

$$\vert x_n[k,l] - x_{n-1}[k,l]\vert > T_n[k,l],$$ (2)

where $T_n[k,l]$ is a threshold that is adaptively determined as described in [6]. It is assumed that the regions significantly different from the background are moving regions. Estimated background image is subtracted from the current image to detect moving regions. In other words, all of the pixels satisfying

$$\vert x_n[k,l] - B_n[k,l]\vert > T_n[k,l]$$ (3)

are determined. These pixels at $[k,l]$ locations are grouped into connected regions (blobs) and labeled by using a two-level connected component labeling algorithm [12]. The output of the first step of the algorithm is a binary pixel map $Blobs[k,l]$ that indicates whether or not the pixel at location $[k,l]$ in image $x$ is moving.

Other more sophisticated methods, including the ones developed by Bagci et al. [2] and Stauffer and Grimson [21], 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 regions as an initial step in the fire and flame detection system. We choose to implement the method suggested by Collins et al. [6], because of its computational efficiency.