Background Subtraction Problems

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Background Subtraction Problems
According to Omar Javed, Khurram Shafique and Mubarak Shah (2001), the main problems of background subtraction approaches can be listed as follows.
Quick illumination changes
Quick illumination changes will affect the color characteristics of the background pixels on color or intensity based subtraction in background model. As a result, whole image appears as foreground. This kind of situation will occur in partially cloudy days.
Relocation of the background object
It induces change in two different sections in the image. It’s continuously acquired its position and previous position. If only the former will be identified as foreground region, any background subtraction system based on color variation is detected
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The tracking of objects in real scene is a difficult task, because of,
1. Image changes can be obscure object features to mislead tracking. For example, noise, shadows, light changes, reflection and clutter.
2. The presence of multiple moving objects. Such as when objects have similar features, if their paths cross or if they occlude each other.
3. The presence of non-rigid and articulated objects and their non-uniform features.
4. Inaccuracy of preceding object segmentation.
5. Changing object features, such as object deformation or scale change.
6. Application related requirements, such as real-time processing.
According to Yu Zhong (2000), many object tracking application share the following properties,
1. In many object tracking applications, the inter frame motion of the object is small, therefore the object in the next frame is in the neighborhood of the object in the current frame.
2. The same point on the objects has a consistent color or gray scale in all the frames of the sequence.
3. Moving object’s boundary has a relatively large motion field.
4. Object’s boundary has a large image gradient
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They are, correspondence to match objects between successive image and explicit tracking using a position prediction strategy or motion estimation.
According to Qi zang and Reinhard Klette (2002), object tracking techniques can classify into four categories. They are,
• Tracking based a moving objects region – This technique identifies and tracks a blob taken or a bounding box. These are calculated for connected components of moving objects in 2D space. The method includes on properties of these blobs. Such as size, color, shape velocity and centroid.
• Tracking based on an active contour of moving object – the attitude of moving object is updated dynamically. It can represent as a “snake”. It relies on the boundary curves of the moving object. For example, it is efficient to track moving people on the road by selecting the contour of human’s head.
• Tracking based on a moving model – basically model based tracking refers to 3D model of moving object. This method includes a parametric 3D geometry of as moving object. It can solve partially occlusion
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