## 2 Vision based Target Tracking for Autonomous Vehicles Navigation
### Visual Target Tracking for Autonomous Land Vehicles Navigation
However most failures of this system are due to the shortcomings of their simple visual target detection algorithms. Their method is also limited to a \(2D\) workspace without considering the problems  
Figure 7: (a) shows the system overview of [57]. (b) is taken from [59] and shows the car models used in their method for vehicles tracking. In [59], the object in the image is assumed to be an instance of one of these four models. in the \(3D\) space. Tracking targets robustly by vision is very difficult for autonomous vehicles running on irregular terrains in natural environment, because the image deformation caused by rolling and pitching of the camera, as well as the relative movement between the target and camera, affects the tracking ability greatly. One approach to cope with such problems is matching the target image with many affine transformed candidate images while tracking. But when the number of candidate images gets larger, such an approach is not available to the real-time tasks due to the computational cost. In [62] [PERSON] _et al._ propose a new Robustness Analysis for Tracking (RAT) to improve the tracking ability. RAT is the analysis based on features of the object image, where three parameters: 'Detectability', 'Robustness for Depth (RBD)' and 'Robustness for Rotation (RBR)' are defined. Much more robust templates can be found by analyzing the object image using RAT before the tracking task is performed. In [63] [PERSON] _et al._ present an approach for scene classifications in dense disparity maps from a binocular stereo map. The classification result is used for the indoor autonomous vehicle tracking and navigation purposes. The \(3D\) model of the scene is also derived directly from the disparity image. The classification of the scene helps to decide, which objects are interesting and should be monitored as well as which behavior is appropriate depending on the current structure. The vehicle can switch from the wall following in hallway environments to localization based on the corner structures, etc. However, the shortcoming of this system is that the applied algorithms used for dynamical composition of tracking primitives are highly dependent on the current environmental structures. In [64] [PERSON] _et al._ propose a ball tracking and catching strategy called GAG (short for \"Gaining Angel of Gaze\") that enables an autonomous vehicle to track and catch a ball flying in the three-dimensional space. The mobile robot receives a visual feedback control scheme based on GAG and then the proposed scheme enables the robot to track and catch a ball flying in the three-dimensional space by using a monocular vision system. In [65] [PERSON] _et al._ propose a correspondence based method, which applies the Iterative Closest Point (ICP) algorithm to match feature points on the ground plain. Since the outliers in the scene contribute false measurements for estimation, they introduce a stereo vision-based method to detect free-space on the road plain. They extract the edge points in the free-space as primitives, which avoid the limit of the rigid scene hypothesis. Other visual target tracking methods for autonomous land vehicles navigation are: the paper [66] deals with the problem of computing the motions of a robot observer in order to maintain the visibility of a moving target; Taking inspiration from the visual system of the fly, the paper [67] describes and characterizes a monolithic analog very large-scale integration sensor, which produces control signals appropriate for the guidance of an autonomous robot to visually track a small moving target; The visual vehicles following systems are also presented in [59, 68] (Figure 7).