# Moving Object Tracking and Its Application to an Indoor Dual-Robot Path
## 1 Introduction  
With the development of human civilization and technologies, human life has changed a lot in the past few decades. One of the technologies that has progressed is robot systems. Intelligent robots are now used in daily life for entertainment, medical care, home security, and services in other fields. Intelligent robots integrate electronics, mechanics, control, automation, and communication technologies. Different types of robots have been developed in recent years to meet a variety of needs. The development of robot systems combines the theoretical expertise of many professionals. Related studies and applications are extensive, including obstacles avoidance, path planning, and visual image processing. How to improve the accuracy of a robot's performance is one of the main foci in the field of intelligent robotic control. Omnidirectional wheeled mobile robot (WMR) is one of the mobile robot models that has been discussed widely. It has more advantages than normal WMR, such as mode of action, easy control, and high mobility [1; 2; 3; 4; 5]. Designing an intelligent system for omnidirectional WMR to search and track moving objects automatically is the objective of this study. When omnidirectional WMR is working, one must consider how to adapt to the environment and improve the efficiency of the mission. Many researchers have presented studies on intelligent robot control. Studies on intelligent robots include aspects such as obstacle avoidance, path planning, object tracking, etc. [PERSON] implemented obstacle avoidance and Zigbee control functions for an omnidirectional mobile robot [6]. [PERSON] applied a omnidirectional mobile robot to remote monitoring [7]. [PERSON] presented wheeled mobile robot obstacle avoidance and object following control based on real-time image tracking and fuzzy theory [8]. [PERSON] proposed multi-sensor surveillance of indoor environments by an autonomous mobile robot. The robot was equipped with a monocular camera, a laser scanner, encoders, and an RFID device, and used a multi-layer decision and control scheme. Fuzzy logic was applied to integrate information from different sensors [9]. [PERSON] utilized an omnidirectional mobile robot for map building applications [10]. [PERSON] et al. proposed three fuzzy control systems for obstacle avoidance, target seeking, and wall following. The fuzzy control system was integrated in the mobile robot and applied to home security patrol [11]. [PERSON] presented intelligent strategies for a WMR to avoid obstacles and move to a target location. In a short-distance obstacle avoidance model, the WMRutilized signals from ultrasonic sensors to avoid obstacles. In a target-driven obstacle avoidance model, fuzzy theory with sensor signals was used to control the speed of the WMR and make it move to a target location [12]. One of the purposes of this study is to use real-time images from a webcam to provide target recognition and tracking, which includes color identification of a destination room. In image recognition, we use the fuzzy color histogram classification, which can quickly and accurately recognize environment background color and patterns. [PERSON] introduced fuzzy naturals-based histograms on fuzzy color spaces. Histograms were fuzzy probability distributions on a fuzzy color space, where the fuzzy probability was calculated as the quotient between a fuzzy natural number and the number of pixels in the image, the former being a fuzzy (non-scalar) cardinality of a fuzzy set [13]. [PERSON] developed a fuzzy color histogram generated by a self-constructing fuzzy cluster that can reduce the interference from lightness changes for the mean shift tracking algorithm. Their experimental results showed that the tracking approach was more robust than the conventional mean shift tracking algorithm and the computation time only increased a little [14]. [PERSON] presented image processing that can be either an image or a set of characteristics or parameters related to an image.