## 2 Vision based Target Tracking for Autonomous Vehicles Navigation
### Visual Target Tracking for Autonomous Land Vehicles Navigation
Using color information of the person's specific appearance, it calculates the centers of masses of the segmented color-blobs in each of the two images that form the conjugate pair of images in a tracking sequence. The current viewing direction of each camera in their system is adjusted so that the center of mass becomes the center of the image frame. However, a change in illumination can induce shifts in the center of mass of the blob being tracked in the two camera images, which makes the target tracking fail. [PERSON] _et al._[44] propose a tracking system employing a visually controlled aerial robot which recognizes the motion of the specified person. They propose the motion recognition technique employing MHIs and eigenspaces. The human region is extracted by its color information. One example of optical flow based autonomous vehicles human following systems can be referred to [PERSON] _et al._[45]. They propose a real-time navigation system which observes the human behavior and reacts to those actions. The system detects body parts as the moving areas, and a face region or region specific to human is then extracted in the detected area based on the skin color or the cloth color of the human. In conclusion all these human following methods detect and track human's motion based on human's special visual features or the additional sensors in the environment. It limits the applications of such algorithms to track unexpected or different objects in an unstructured outdoor environment. Localization and target-tracking are both challenging yet essential and have a wide range of applications in mobile robotics. Localization can be defined as determining the position of an object within a reference coordinate system, and tracking consists of constructing a trajectory given a collection of spatially and temporally coherent localizations. Localization, mapping and moving object tracking serve as the basis for scene understanding, which is a key prerequisite for making a robot truly autonomous. Simultaneous localization, mapping and moving object tracking (SLAMOT) involves not only simultaneous localization and mapping (SLAM) in dynamic environments but also detecting and tracking these dynamic objects [46]. Several methods have been proposed using visual target tracking for autonomous vehicles localization and map building. In [47] [PERSON] _et al._ present an algorithm for visual position tracking of individual cooperative autonomous vehicles within their working environment. Initially, they present a technique suitable for  
Figure 4: (a) is taken from [40]. This figure shows [PERSON] _et al._’s human following algorithm’s system setup and their experimental testing environment. Such an ISpace is specially designed for their algorithm. (b) is taken from [43], which shows their experimental environment for person following. Their system is designed specially based on the person clothes’ color. visual servoing of an autonomous vehicle towards its landmark targets. Secondly, they present an image processing technique that utilizes images from a remote surveillance camera for localization of the robots within the operational environment. In their algorithm the surveillance cameras can be either stationary or mobile. The supervisory control system keeps tracking of relative locations of individual autonomous vehicles and utilizes relative coordinates information of autonomous vehicles to coordinate their cooperative activities. [PERSON] _et al._[48] present a real-time mobile navigation system and an approach for the vision-based Simultaneous Localization and Mapping (SLAM) based on a second generation of the image processing library, XVision. They show how the multiple tracking and low-level image processing primitives for color, texture and disparity can be combined to produce vision-guided navigation systems. The applications they discuss make use of XVision capabilities to solve the temporal correspondence problem by tracking an image feature in a given image domain.