# ROS-Based Human Detection and Tracking from a Wireless Controlled Mobile Robot Using Kinect
## 2 Literature Survey
The second step is the detection of a targeted human using a matching process between the foreground and the target image based on the Principle Component Analysis (PCA) algorithm [7], which is a method of transforming a number of correlated variables into a smaller number of uncorrelated variables based on eigen-decomposition. The final step is using a Kalman filtering algorithm [8] to track the detected human. In reference [9], [PERSON] et al. proposed a real-time robotic system capable of mapping indoor, cluttered environments, detecting people, and localizing them in the map using a RGB-D camera mounted on top of a mobile robot. The proposed system integrates a grid-based simultaneous localization and mapping approach with a point cloud-based people detection method to localize both the robot and the detected people in the same map coordinate system. In reference [10], [PERSON] et al. proposed a real-time RGB-D based multi-person detection and tracking system suitable for mobile robots and head-worn cameras. The proposed vision system combines two different detectors for different distance ranges. For the close range, the system enables a fast depth-based upper-body detector, which allows real-time performance on a single core processing unit (CPU) core. For the farther distance ranges, the system switches to an appearance-based full-body Histogram-of-Oriented-Gradient (HOG) detector running on the graphic processing unit (GPU). By combining these two detectors, the robustness of the vision system using an RGB-D camera can be improved for indoor settings. [PERSON] et al. [11] proposed a stereo vision based human detection and tracking system, which is integrated with the help of image processing. To identify the presence of human beings, an HSV-based skin color detection algorithm is used. When the system identifies the presence of human beings, the human location is then identified by applying a stereo vision-based depth estimation approach. If the intruder is moving, the centroid position of the human with the orientation angle of motion is obtained. In the tracking part, the human tracking system is a separate hardware system connected to two servo motors for targeting the intruder. Both human detection and human tracking systems are separately coupled through a serial port. If there are several people present on the video or image, then the centroid of all the regions are estimated and the midpoint of them gives the required result. In reference [12], [PERSON] proposed an autonomous human following robot, which comprises face detection, leg detection, color detection, and person blob detection methods based on RGB-D and laser sensors. The authors used Robot Operating System (ROS) to perform the four detection methods, and the result showed that the robot could track and follow the target person based on the person's movement. As to the wireless control of the mobile platforms, [PERSON] et al. [13] proposed a smart wheelchair, which can be useful assistance to physically disabled persons to complete their daily needs. The previous development of this smart wheelchair system was developed using a personal computer or laptop in the wheelchair. Because recent developments in embedded systems can be combined to the design of robotics, artificial intelligence, and automation control, the authors further integrated a Radio Frequency (RF) communication module into the smart wheelchair system to implement wireless control function in the wheelchair. Moreover, the authors used an acceleration sensor to implement a head gesture recognition module, which allows the user to control the wheelchair in directions as needed to move, using the change of head gesture only. In reference [14], [PERSON] et al. proposed the hardware architecture for a mobile heterogeneous robot swarm system, in which each robot has a distinct type of hardware compared to the other robots.