Although the performance of CCD and CMOS imaging sensors has improved since their invention, they still have several physical limitations, such as various sources of noise, limited dynamic range, and limited spatial resolution. Besides these physical limitations, they have malfunctioning problems, such as smearing and blooming, which degrade the quality of captured images. These limitations and malfunctioning problems can be overcome, based on device physics and circuit technology. However, a signal-processingbased approach is a good alternative solution to these problems, because it may cost less and existing imaging systems can be still utilized. In a broad sense, this signal-processing-based approach can be called a super-resolution approach. The goal of this article is to introduce a super-resolution approach that overcomes the limitations of imaging sensors. To this purpose, we describe the existing limitations of imaging sensors first, and then describe the corresponding super-resolution approach.

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Recently, kernel-based tracking algorithms such as the mean shift tracking algorithm has been proposed, which use the information of color histogram together with some spatial information provided by the kernel. However, in spite of the fast speed, there exists an inherent instability problem which is due to the use of an isotropic kernel for spatiality and the use of the Bhattacharyya coefficient as the similarity function. In this paper, we will analyze how the use of the kernel and the Bhattacharyya coefficient can arouse the instability problem. Based on the analysis, we propose a tracking scheme that uses a new representation of the location of the target which is constrained by the color, the area, and the spatiality information of the target in a more stable way than the mean shift algorithm. With this representation, the target localization in the next frame can be achieved by a direct one step computation, and the tracking becomes stable, even in difficult situations such as low-rate-frame environment, and partial occlusion.

Recently, mean shift tracking algorithms have been proposed which use the information of color histogram together with some spatial information provided by the kernel. In spite of their fast speed, the algorithms are suffer from an inherent instability problem which is due to the use of an isotropic kernel for spatiality and the use of the Bhattacharyya coefficient as a similarity function. In this paper, we analyze how the kernel and the Bhattacharyya coefficient can arouse the instability problem. Based on the analysis, we propose a novel tracking scheme that uses a new representation of the location of the target which is constrained by the color, the area, and the spatiality information of the target in a more stable way than the mean shift algorithm. With this representation, the target localization in the next frame can be achieved by one step computation, which makes the tracking stable, even in difficult situations such as low-rate-frame environment, and partial occlusion.

In this paper, we propose a stable scale adaptive tracking method that uses centroids of the target colors. Most scale adaptive tracking methods have utilized histograms to determine target window sizes.However, in certain cases, histograms fail to provide good estimates of target sizes, for example, in the case of occlusion or the appearance of colors in the background that are similar to the target colors. This is due to the fact that histograms are related to the numbers of pixels that correspond to the target colors. Therefore, we propose the use of centroids that correspond to the target colors in the scale adaptation algorithm, since centroids are less sensitive to changes in the number of pixels that correspond to the target colors. Due to the spatial information inherent in centroids, a direct relationship can be established between centroids and the scale of target regions. Generally, after the zooming factors that correspond to all the target colors are calculated, the unreliable zooming factors are filtered out to produce a reliable zooming factor that determines the new scale of the target. Combined with the centroid based tracking algorithm, the proposed scale adaptation method results in a stable scale adaptive tracking algorithm. It tracks objects in a stable way, even when the background colors are similar to the colors of the object.