## 2 Vision based Target Tracking for Autonomous Vehicles Navigation
### Visual Target Tracking for Autonomous Land Vehicles Navigation
They give a description of the operation process for the vision subsystem, and propose an adaptive segmentation method based on the color information. Color information is the foundation of their object identification. In MRFS, halls, obstacles and robot fishes are equipped with specified color properties. In [56] the problem of estimating and tracking the motion of a moving target by a team of mobile robots is studied. Each robot is assumed to have a directional sensor with limited ranges, thus more than one robot (sensor) is needed for solving the problem. A sensor fusion scheme based on the inter-robotcommunication is proposed in order to obtain the accurate real-time information of the target's position and motion. Accordingly a hierarchical control scheme is applied, in which a consecutive set of desired formations are planned through a discrete model and low-level continuous controls are executed to track the resulting references. [PERSON] _et al._[57] describe a tracking system relying on active contours for the target exaction and an Extended Kalman Filter for the relative pose estimation (Figure 7). Their work represents the first step towards treating the general problem for the control of several unmanned autonomous vehicles flying in formulation using visual information. The Extended Kalman Filter is improved by introducing additional image information available to the vehicle with a fixed forward-pointing monocular camera. Active contours are used to track the follower in the image plain and provide the Kalman Filter with the required input. There are several drawbacks in this kind of methods. They ignore the additional angle measurements found in the equivalent \(3D\) range estimations and also they do not consider the target's acceleration effects. In [58] [PERSON] _et al._ have formulated an algorithm that has coordinated the movements of multiple robots to follow a search tactic collectively in an unknown and cluttered environment. First they develop the individual robot reactive behavior that make their coordinated movement possible. Their algorithm requires every robot to be programmed with the same set of primitive behaviors: (1) obstacles negotiation; (2) homing; (3) flocking and (4) searching, with obstacles negotiation being the most important and searching being the least important. According to different environment stimulants, the robots adopt one of these behaviors at a time according to their order of importance for the cooperation purposes. Multiple mobile robots (autonomous vehicles) cooperation is a rather hot and promising research topic. Many researchers already consider it as their future working directions, such as the multiple mobile robots visual target tracking or multiple mobile robots SLAM. It can be foreseen that in the near future more and more systems will be developed in this area. Figure 6: These two figures are taken from [52]. (a) shows the overview of their proposed system. (b) is the block diagram of their tracking system. The traditional visual target tracking techniques usually ignore the presence of obstacles and focus on imaging and target recognition issues. The papers [60; 61] introduce a new visual tracking algorithm for autonomous vehicles navigation when the target moves unpredictably and no prior map of the environment exists. Their algorithm computes a motion strategy based exclusively on the current sensor information, while no global map or historical sensor data is required. The algorithm is based on the notion of escape risk and the computation of an escape-path tree. Their proposed algorithm governs the motion of autonomous vehicles based on current measurements of the target's position and the location of the local obstacles. Their approach is combinatorial in the sense that the algorithm explicitly computes a description of the geometric arrangement between the target and observer's visibility region produced by the local obstacles. The algorithm computes a continuous control law based on this description.