## 2 Vision based Target Tracking for Autonomous Vehicles Navigation
### Visual Target Tracking for Autonomous Land Vehicles Navigation
Normally it is not easy to keep tracking the human back if the background is so complex and cluttered. They solve the problem by choosing the texture of clothes and the human shoulder images as the template patterns to be detected and identified. This tracking system requires to know the special visual features of the target (the texture of the clothes and human shoulder). Such tracking systems are not suitable to track unknown objects appearing in an unknown situation. One kind of research work on autonomous vehicles human following can be found from [PERSON] _et al._[39, 40, 41]. According to [PERSON] _et al._[40]'s human-following work, an intelligent space (ISpace), which is an intelligent environment with many intelligent sensors, is provided. The autonomous vehicle  
Figure 3: (a) is taken from [34], which shows their self-similar landmark pattern with barcode for landmarks detection and tracking. (b) is taken from [35]. This figure shows the structure of landmark, which they use for the self localization of indoor mobile robots. cooperates with multiple intelligent sensors, which are distributed in the ISpace. The distributed sensors recognize the target human and the autonomous vehicle, and give control commands to the robot. CCD cameras are used as a kind of sensors of DINDs (Distributed Intelligent Network Devices) for ISpace. Location information of the human and autonomous vehicle is obtained by stereo vision processing and human do not need to hold any special tags (Figure 5). However there is a major drawback with this kind of human-following methods. The system strictly needs the ISpace. Normally for an unknown or unstructured environment, such an ISpace is not available, thus in order to achieve the visual target tracking, the vehicle's onboard sensors should mostly provide the accurate target dynamic information. [PERSON] _et al._ design an autonomous vehicle, which at the same time executes a pre-programmed tour in a public exposition and allows for complex, collaborative interactions with the non-experienced visitors. In their system, they have dedicated two tasks to gather the information of the human presence in a public environment: a color camera based face tracking and a motion tracking based on the information from the laser ranger finder. The main steps of visual face detection and tracking in their system include: the skin color detection; contour extraction and filtering; tracking. Information gathered from the face tracking together with the motion tracking helps to verify the presence of the visitors. [PERSON] _et al._ present a humanoid walking control system that generates body trajectories to follow a given desired motion on-line. They implement the system by making the autonomous vehicle track and follow a moving person based on the stereo vision feedback. The visual tracking consists of \(3\) parts: a stereo vision processing for target detection and \(3D\) position estimation in the camera coordinates; a planning of the desired future torso movements during one step; a camera posture and gaze direction control with the self-motion compensation. While the autonomous vehicle and human are both moving, the color segmentation and thresholds are utilized to detect the relative human's direction. Then a real-time depth map generation algorithm is employed to measure the distance to the human. [PERSON] _et al._[43] present an efficient human following algorithm for an autonomous vehicle using two independent moving cameras. In order to control the camera's pan/tilt motions, they have presented an image-based PTU control algorithm using a lookup table that stores the correspondences between the camera pan/tilt angles required to keep a target in the center of the image frame and the pixel displacements produced by the target in the image plain (Figure 4). The major problem with this method is that the object tracking is accomplished with a simple color histogram based algorithm.