## 2 Vision based Target Tracking for Autonomous Vehicles Navigation
### Visual Target Tracking for Autonomous Land Vehicles Navigation  
In general applications of visual target tracking for Autonomous Land Vehicles navigation include some sorts of landmarks tracking, people following, multiple mobile robots cooperation, vehicles localization and map building, visual target tracking with pan-tilt cameras platforms and so on. According to their applications, different methods are categorized and reviewed as follows. It should be pointed out that the classification in this part is not absolute because algorithms from different categories can be intergraded together to achieve the navigation goal. Visual Landmark Tracking for Autonomous Land Vehicles Navigation  
Visual landmark tracking is one kind of vision-based methods for autonomous land vehicles navigation. Landmarks are divided into two classes: natural or artificial. Generally natural landmarks are selected in the scenes in consideration of their particular characteristics. Autonomous vehicles learn those characteristics or keep the features of the landmarks in memory and recognize them using neural network or some matching techniques while they move [30; 31; 32]. Mosaic images of outdoor environments are also used for the image matching based method in [33]. On the other hand an artificial landmark is often designed with a specific pattern or color in consideration of its detection algorithm. For example, a landmark that has a bar code or a specific shape pattern such as the sine waves has been proposed. Recently, [PERSON] _et al._[34] use the self-similar gray pattern landmarks for navigation and localization aids (Figure 3). In [35] [PERSON] _et al._ propose a simple artificial landmark model, which can be used for the self localization of indoor mobile robots (Figure 3). In their paper an effective visual detection and tracking algorithm for this landmark is proposed. A pair of color points neighboring each other have been used as a sample to represent the probability density in the Condensation algorithm. Under the assumption of affine cameras and only with the information of a single landmark in a single image, they have presented a localization algorithm to estimate the absolute position accurately. In [36] [PERSON] _et al._ propose a visual landmark tracking algorithm for docking unicycle-like vehicles based on the hearing-only information. An omni-directional panoramic camera is used to detect visual landmarks around the docking station and provide bearing (or heading) data for each observed landmark. In their method a robust and computationally cheap visual blob detection algorithm is proposed. The artificial landmarks used consist of two adjacent large color blobs. Using two adjacent color blobs improves the noise rejection over the single color blob extraction algorithms. In [37] [PERSON] describes method and system for enabling semi-autonomous or autonomous vehicle travel includes providing a vehicle travel management system which monitors the location of vehicles in atravel lane and the location of the travel lane, creating dedicated travel lanes for vehicles equipped with the vehicle travel management system, and managing travel of vehicles in the dedicated travel lanes to maximize travel speed of vehicles and minimize collisions between vehicles. Normally, the robust extraction of natural landmarks is a difficult task. And the artificial landmark methods that use peculiar contour, color or edge information highly depend on the low-level image processing results, which are influenced by the noise and de-focus phenomenon very much. Also, artificial landmark methods are not robust under the geometrical background variations such as the rotation, camera zoom and viewing direction changes. Human Following for Autonomous Land Vehicles Navigation  
Several approaches are proposed to use visual target tracking for human following by autonomous vehicles. In [38] [PERSON] _et al._ present a visual tracking system for a human collaborative mobile robot. The robot tracks the human back and shoulder to follow a person.