# Moving Object Tracking and Its Application to an Indoor Dual-Robot Path
## 1 Introduction
The Kalman filter was applied to reduce the noise and estimate human's motion state. The mobile robot was controlled to track moving humans in the view of Kinect [23]. [PERSON] et al. proposed a method of frame relative displacement in which a multi-agent robot can be used for tracking, tooling, or handling operations with the use of stereo vision in an unstructured laboratory environment. The relative position between the robot's tool center point and the object of interest is essential information to the robot system. The system has two robot arms: one carries a stereo vision camera system and the other is guided in relation to the object of interest. A marker is used for navigation between the robot and the object of interest. Image processing, marker detection, 3D coordinates extraction, coordinate system transformations, offset coordinates calculation, and communication are handled using the C++ program and TCP/IP protocol [24]. The differences in our study are that a cheaper webcam is applied to catch moving objects by the use of the mean shift method. Information on the detected object can be sent to a control center via well-installed Wi-Fi communication. Omnidirectional WMR, instead of a two-wheel- or four-wheel-driven robot, is utilized for easy direction control. An intelligent omnidirectional WMR integrates many functions, such as environment sensing, dynamic decision-making and tracking, behavior control and executing, etc. In WMR movement control, the ultrasound sensor is one of the most used sensors to avoid obstacles [8]. In [25], fuzzy logic was used to form parking behavior patterns for path planning. Reference values were searched by a genetic algorithm and applied to obtain the optimal WMR tracking performance. [PERSON] applied a laser sensor and A* algorithm to a WMR that can avoid obstacles and planed moving path [5]. Many researchers have tried to improve robot systems to make them easy to control, which will help humans and robots to interact more harmoniously in daily life. The main purpose of this study is to integrate a webcam, ultrasound sensor, and RFID reader in two omnidirectional WMRs for indoor patrol. In path planning, the WMR used a webcam to search predefined doors for destination tracking and used ultrasound sensors for obstacle detection. Fuzzy color histogram classification is used to separate moving objects from the background environment. In this study, a mean shift algorithm is applied to classify the object from the background and track moving objects. An RFID reader is used to read the tag and check whether it is the destination or not. By image recognition and path planning, the omnidirectional WMR can automatically search and track moving objects. Omnidirectional WMR is a nonlinear model; a dynamic equation is needed for system analysis and control regulation design. The fuzzy system is applied in fuzzy controller design for its nonlinearity, and the control scheme is simple and flexible. Ultrasonic sensors receive reflecting values when the WMR moves, then these values are used to determine the locations of the obstacles and as inputs of the fuzzy controller to avoid obstacles. The mean shift method can find the highest color density of sample points from the fuzzy color space of the target object and keep tracking it. The contributions of this study are as follows: the proposed control scheme can control omnidirectional WMR to search and track moving objects with real-time image processing and plan a path automatically; the RFID reader identifies destination again when omnidirectional WMR moves forward to the destination; obstacles avoidance is controlled by a fuzzy controller; intruders can be tracked by the mean shift algorithm, and the image of the intruder can be sent to the control center via Wi-Fi communications from the robot; and the proposed dual robot patrol system can perform indoor security service and release human resources. This paper is divided into five sections. The first section is the introduction of this paper. The other five sections are as follows.