# Moving Object Tracking and Its Application to an Indoor Dual-Robot Path
## 1 Introduction
The color vision system first classified pixels in a given image into a discrete set of color classes. The objective was to produce a fuzzy system for color classification and image segmentation with the least number of rules and the minimum error rate. Fuzzy sets were defined for the H, S, and L components of the hue, saturation, and lightness (HSL) color [15]. [PERSON] proposed an image segmentation scheme based on a fuzzy color similarity measure to segment out meaningful objects in an image according to human perception. The scheme defined a set of fuzzy colors based on the HLS color coordinate space. Each pixel in an image is represented by a set of fuzzy colors that were the most similar colors in the color palette selected by humans. Then, a fuzzy similarity measure was proposed for evaluating the similarity of fuzzy colors between two pixels. He recursively merged adjacent pixels to form meaningful objects by the fuzzy similarity measure until there was no similar color between adjacent pixels [16]. [PERSON] proposed a scheme with swimmer detection and swimmer tracking stages. The detection stage began with employing the mean-shift algorithm to cluster input image, and then choosing the sets using graphical models to train the Gaussian mixture model. Finally, swimmers can be detected in a model-based way [17]. To identify a human object, [PERSON] presented a human detection algorithm embedded into a thermal imaging system to form a fast and effective trespasser detection system for smart surveillance purpose. A pattern recognition technique was employed whereby the human head was detected and analyzed for its shape, dimension, and position [18]. [PERSON] presented data fusion acquired from a CCD camera and a thermal imager. The fusion was realized by means of spatial data from a TOF camera to ensure \"natural\" representation of a robot's environment; thus, the thermal and color-related data comprised one stereo image presented to a binocular, head-mounted display [19]. [PERSON] developed four methods to identify persons during tracking. The first method used color information extracted from camera images to distinguish between persons, based on the color of the clothes they wear. The second method used reflectance intensities, which can be provided directly by laser range sensors. The third method was also based on camera information, but employs a probabilistic shape and motion model for each person it tracks, in order to distinguish between them. The last one used a network of dedicated sensors, which directly transmit identification information for each person [20]. [PERSON] proposed a system containing three components: image capture, image pre-processing, and environment determination. The image was first captured by a webcam. Then image pre-processing was used to obtain the moving pixel area by Background Subtraction in order to conduct smoothing filtering and image binary. Finally, moving objects were determined by calculating the variations within an image moving region [21]. [PERSON] developed a computer vision based on moving object detection and automatic tracking system. He used the temporal frame difference technique to find the moving pixels for moving object detection. The temporal frame difference technique can quickly locate all moving pixels and improve computation efficiency. However, if there is a dynamic background,moving pixels will include the background; therefore he first applied global motion compensation in order to reduce the moving background effect. Then he calculated the standard deviation and the maximum of each block. After statistical analysis, the moving object area can be obtained. The use of mean shift iteration can accurately and efficiently calculate the most similar image mass center location and accomplish tracking moving object [22]. [PERSON] et al. proposed a tracking control system of human motion with Kinect onboard a mobile robot. The 3D position information on humans obtained from Kinect can be used to control the velocity and attitude of the robot.