# An Effective Motion-Tracking Scheme for Machine-Learning Applications in Noisy Videos
## 1 Introduction  
Finding a specific object in an image can be used in many ways in our lives. This technology can be used to check whether restricted items are brought in when entering a specific place and can be used to check the presence of harmful objects. Under this circumstance, it can be said that identifying an object of interest in a video rather than a still image is a more important technique. Any information generated after identifying an important object in a video shot in real time can be used for making necessary decisions in the fields of security or medical care. However, it is difficult to track an object of interest in an environment in which the quality of a captured image is not sufficiently good, for example, in a microscopic medical field, a night security image, etc., due to image noise or ambiguity of the object. To overcome this problem, we proposed a technique for tracking a moving object of interest in a noisy video. In general, various methods using machine learning have been proposed to track objects in images [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. However, when the shape of the object to be tracked cannot be specified due to the low quality of the image, it is difficult to construct the training data. Because of this, it can be difficult to apply machine learning itself. To track an object without a separate data set in such an environment, we proposed an object detection and tracking algorithm based on the simplest color balance (SCB) technique [21]. The proposed method unifies the image information corresponding to the object to be detected through the SCB algorithm. The \"information\" refers to the pixel's value in this paper. Then, the unified pixels and the remaining pixels are separated and binarized into the object of interest and the background, respectively. Finally, only the desired object is extracted from the binarized image. The features of the SCB algorithm are as follows. The SCB specifies the upper and lower value ranges of pixels in the image. Thereafter, pixels within the corresponding range are collectively replaced with the minimum and maximum values of the available pixel values. For example, in the case of an 8-bit scale, 0 and 255 are designated, respectively. Afterwards, the histogram of pixel values that do not belong to the lower and upper ranges is stretched as much as possible within the given value scale and normalized to improve the quality, including the color balance of the image. In the task we need to process, the object of interest in the captured image is usually darker than the background. In addition, we did not consider the improvement of image quality when applying the SCB algorithm. In this paper, we adopted the idea of collectively minimizing a certain low range of values in the SCB algorithm. In other words, we selected a value range that can be judged as an interested object among the dark pixels of the image. Then, SCB was applied to collectively substitute 0 for values smaller than the corresponding value. We did not aim to improve the quality of the video. Therefore, the calculation on the bright part was unnecessary. For this reason, the operation for the upper limit range was not executed to increase the efficiency. We also modified the normalization function used in the SCB algorithm to limit the range from 0 to the average value of the histogram of the original image. As a result, the scale information of the background part was compressed after applying the SCB. This was to minimize false negatives that may occur due to meaningless differences between the pixel values of the object to be detected. By applying the modified SCB to the histogram-restricted image, all pixels of the object to be detected were set to 0, and then the binarization algorithm was applied. The binarization algorithm is designed to return 1 if there is a sharp change in the pixel boundary, and 0 otherwise. As a result, when the binarization is completed, the object to be detected is made of 1 and is displayed in white in the image.