## 1 Introduction  
At present, the usage of autonomous vehicles is growing especially in applications such as manufacturing, hazardous materials handling, surveillance, etc. The basic task in any such application is the perception of the environment through one or more sensors. Processing of the sensor input results in a particular representation of the unknown environment, which can then be used for navigating and controlling the vehicle. Autonomous vehicle navigation in a certain environment is thus a quest that many researchers have tackled over the years. The general sensors used for autonomous vehicles include infra-red, sonar, laser, radar and so on [1]. For example, [PERSON] [2] discusses a navigation and control system including an emitter sensor configured to locate objects in a predetermined field of view from a vehicle; [PERSON] _et al._[3] describe the techniques which make use of characteristics of infrared sensitive video data, in which heat emitting objects appear as hot spots. Compared to these types of sensors, vision sensors provide a whole new way for autonomous vehicles to create an image of the environment [4, 5, 6, 7]. Video images plus specialized computer vision algorithms can provide high resolution information concerning the shape or range of nearby objects and environment. Coupled with the availability of increased computational power, visual sensor information becomes not only appealing but also easily attainable in real-time. During the past ten years much research has gone into the area of computer vision for autonomous vehicles navigation [4]. Many algorithms and methods have been proposed, all with an ultimate common goal: to give intelligence to autonomous vehicles to interpret the visual information. If the research goal is to send an autonomous vehicle from one coordinate location to another, there is sufficient accumulated expertise in the research community today to design algorithms which could do that in a typical environment. But if the goal is to carry out the function-driven navigation, such as chasing or following moving targets, avoiding the obstacle which is somewhere in a given hallway and stopping at a stop sign (e.g. docking) under varying illumination and background conditions, it is still eons away. It is still the central research problem for vision based autonomous vehicle navigation that an autonomous vehicles must be aware of the position and dynamic information of the certain moving objects encountered in the environment. Therefore this paper reviews the recent techniques in vision based target tracking for autonomous vehicles navigation. There are, of course, many approaches, and the publications list will be too long if including all of them. When people read such a broad survey paper, they will miss the key points and milestones. Thus this paper surveys only those contributions in the last decade that the authors believe are interesting and important. One thing to be mentioned is that normally CCD (Charge-Coupled Device) cameras are used as vision sensors for autonomous vehicles navigation. CCD cameras' installation and maintenance costs are quite minimal and stereo CCD cameras can also provide the three-dimensional (\(3D\)) scene analysis [5]. For example, a system and method is presented in [8] for efficiently locating in \(3D\) an object of interest in a target scene using video information captured by a plurality of CCD cameras. This system and method provide multi-camera visual odometry wherein pose estimates are generated for each camera by all of the cameras in the multi-camera configuration. The position and velocity of the target relative to the vehicle can be established continually by processing the stream of the cameras images, and this information can be used to navigate the vehicle.