## 1 Introduction
### Applications of Visual Target Tracking for Autonomous Vehicles Navigation
Such collision avoidance problems between human and robots can be called as interaction based on the position information [18], which can be solved by visual target tracking. In security or surveillance applications, it is often more important to carefully position multiple sensors in order to cover a large structured environment. In this content, autonomous vehicle-based trackers are attractive; they can potentially reduce the number of sensors needed in the tracking network, and they should be able to adapt to the movements of the targets or the dynamic changes in an environment by re-positioning themselves in responses [19]. In the underwater environment visual target tracking can be used for Autonomous Underwater Vehicles (AUV) navigation. For example in order to study the behavioral patterns of unknown underwater life forms, it is necessary to observe them carefully for a longer period. It is therefore important for AUVs to stay close to the object being observed by moving with it such as fish following. Also, for a reliable functioning of underwater man-made systems, it is necessary to maintain them by routine observations. Therefore, the underwater exploration and maintenance requires the AUV to have proper observations and as a result close tracking and following along or with these objects is necessary [21] (Figure 2). [PERSON] and [PERSON] [20] propose a sensor fusion technique for Autonomous Underwater Vehicles (the test bed vehicle Twin-Burger 2 in University of Tokyo (Figure 2) to track and follow underwater cables through video images. Also [PERSON] _et al._[22] propose the vision based tracking system for underwater docking. There are also many other robotics applications requiring the visual obstacle avoidance and/or object identification in unstructured underwater environments. For example the U.S. Navy is investigating the use of autonomous underwater vehicles to accurately detect and classify underwater mines prior to beach landings in the hostile territory [23]. Some other applications of visual target tracking systems for autonomous vehicles navigation can also be in the following areas:* The mobile robots soccer [24, 25] (Figure 1). * Obstacle avoidance for the robot manipulators in factory automation doing repetitive and dull work [26]. * Obstacle avoidance for the robotics motion planning [27]. * The motion control of autonomous vehicles in the car manufacturing industry. Many car manufacturers plan to equip their vehicles in the near future with computer-aided visual target tracking capabilities for parallel parking or automatic stop-and-go mode in traffic jams [28]. In order to successfully achieve all the above applications, it is very useful and necessary for autonomous vehicles to have the knowledge of the \(3D\) dynamic information of the objects of interest in the environment. Therefore, it is very beneficial to write such a survey paper to highlight and summarize the recent interesting techniques on this topic. This rest of paper is organized as follows: In Section 2, the algorithms for autonomous land, underwater, aerial vehicles are reviewed separately. Next data fusion based methods for autonomous vehicles navigation are reviewed in Section 3. In Section 4, based on the previous reviews, remaining research problems are concluded and future research directions are identified. Section 5 draws the conclusion of this paper. In our paper various object tracking methods will not be fully covered, because it is really out of scope of our main topic for autonomous vehicles navigation. However [PERSON] _et al._[29] survey most of state-of-art object tracking methods. In their paper, they also mention that object tracking can be widely used for vehicle navigation, such as video-based path planning and obstacle avoidance capabilities. They review: object representation methods; feature selection methods for tracking; object detection methods and object tracking methods such as Point Tracking, Kernel Tracking, Silhouette Tracking. This paper can be good supplements for our survey paper.