A moving target detection algorithm based on the dynamic background

The final dam corresponds to the watershed lines which are the desired segmentation result.

The principle applications of the method are in the extraction of uniform objects from the background. Regions are characterized by small variations in gray levels, have small gradient values. So it is applied to the gradient than the image. Region with minimum correlated with the small value of gradient corresponding to the objects of interest.

Use of Motion in segmentation:

Motion of objects can be very important tool to exploit when the background detail is irrelevant. This technique is very common in sensing applications.

Let us consider two image frames at time t1 and t2, f(x,y,t1) and f(x,y,t2) and compare them pixel to pixel. One method to compare is to take the difference of the pixels

D12(x,y) = 1 if | f(x,y,t1) – f(x,y,t2)| > T,

= 0 otherwise.

Where T is a threshold value.

This threshold is to signify that only when the there is a appreciable change in the gray level, the pixels are considered to be different.

In dynamic image processing the D12 has value set to 1 when the pixels are different; to signify the objects are in motion.

Image segmentation using edge flow techniques:

A region-based method usually proceeds as follows: the image is partitioned into connected regions by grouping neighboring pixels of similar intensity levels. Adjacent

regions are then merged under some criterion involving perhaps homogeneity or sharpness of region boundaries.

Over stringent criteria create fragmentation; lenient ones overlook blurred boundaries and over-merge. Hybrid techniques using a mix of the methods above are also popular.

A connectivity-preserving relaxation-based segmentation method, usually referred to as the active contour model, was proposed recently. The main idea is to start with some initial boundary shape represented in the form of spline curves, and iteratively modifies it by applying various shrink/expansion operations according to some energy function. Although the energy-minimizing model is not new, coupling it with the maintenance of an ``elastic'' contour model gives it an interesting new twist. As usual with such methods, getting trapped into a local minimum is a risk against which one must guard; this is no easy task.

In [2], the authors create a combined method that integrates the edge flow vector field to the curve evolution framework.

Theory and algorithm of Edge flow and curve evolution:

Active contours and curve evolution methods usually define an initial contour C0 and deform it towards the object boundary. The problem is usually formulated using partial differential equations (PDE). Curve evolution methods can utilize edge information, regional properties or a combination of them. Edge-based active contours try to fit an initial closed contour to an edge function generated from the original image. The edges in this edge function are not connected, so they don't identify regions by themselves.

An initial closed contour is slowly modified until it fits on the nearby edges.

Let C(ϕ ):[0,1] →R2 be a parameterization of a 2-D closed curve. A fairly general curve evolution can be written as:

where κ is the curvature of the curve, N is the normal vector to the curve, , α β are constants, and S is an underlying velocity field whose direction and strength

depend on the time and position but not on the curve front itself. This equation will evolve the curve in the normal direction. The first term is a constant speed parameter that

expands or shrinks the curve, second term uses the curvature to make sure that the curve stays mooth at all times and the third term guides the curve according to an

independent velocity field.

In their independent and parallel works, Caselles et al.and Malladi et al. initialize a small curve inside one of the object regions and let the curve evolve until it reaches the object boundary. The evolution of the curve is controlled by the local gradient. This can be formulated by modifying (1) as:

(2)

Edge flow image segmentation [3] is a recently proposed method that is based on filtering and vector diffusion techniques. Its effectiveness has been demonstrated on a large class of images. It features multiscale capabilities and uses multiple image attributes such as intensity, texture or color. As a first step, a vector field is defined on the pixels of the image grid. At each pixel, the vector’s direction is oriented towards the closest image discontinuity at a predefined scale. The magnitude of the vectors depends on the strength and the distance of the discontinuity. After generating this vector field, a vector diffusion algorithm is applied to detect the edges. This step is followed by edge linking and region merging to achieve a partitioning of the image. Details can be found at [3].

Summary

IV. ANALYSIS AND COMPARISON OF THE TWO TYPES OF

MOTION DETECTION ALGORITHM

Intelligent visual surveillance system can be used many different methods for detection of moving targets, A typical method such as background subtraction method, frame difference method. These methods have advantages and disadvantages, the following will be introduced. A. Background subtraction method Background subtraction method is a technique using the difference between the current image and background image to detect moving targets. Process flow chart is shown as Fig.