## 2 Vision based Target Tracking for Autonomous Vehicles Navigation
### Visual Target Tracking for Autonomous Aerial Vehicles  
Development of Autonomous Aerial Vehicles (AAV) (such as the unmanned helicopters) has been an active area of research for several years. AAVs have been used as test beds to investigate problems ranging from control, navigation, and path planing to object tracking and following [69]. An early approach to the AAV landing [70] decouples the landing problem from vision-based target tracking. Nowadays several techniques have been implemented for vision based landing of an AAV on stationary or moving targets [70]. The problem of landing as such is inherently difficult because of the instability of the AAV near the ground [71]. Also, since the dynamics of a helicopter is nonlinear, only an approximate model of the helicopter can be constructed [69]. In [72] a vision-based target tracking approach to the AAV landing is presented. Their landing pad has a unique shape which makes the problem of identification of the landing pad much simpler. In [73], a vision-based solution is given for the safe landing-site detection in the unstructured terrain, where the key problem is for the onboard vision system to detect a suitable place to land without the aid of a structured landmark, such as a helipad. The University of California, Berkeley (UC Berkeley) team has proposed a real-time computer vision system for tracking a landing target [71, 74] and have successfully coupled it with a helicopter controller to achieve landing [75]. In [76] [PERSON] _et al._ have presented the design and implementation of a real-time vision-based system for detecting a landing target (stationary or in intermittent motions) and a controller to autonomously land the AAV on the target. Their method relies on the assumptions that the landing target has a well-defined geometric shape, and all the feature points of the landing target are coplanar. In [76] they use invariant moment descriptors for detecting and landing the AAV on the target. They do not impose any restriction on the shape of the landing pad except that it is planar. However there exists the problem of safely and precisely landing the AAV in the unstructured harsh \(3D\) environment. The visual target tracking approach from [69] differs from the prior approaches in two ways (Figure 9). First, they impose no constraints on the design of the landing pad except that it should lie on a two-dimensional plain. Secondly, they use moment descriptors to determine the location and orientation of the landing target. Their algorithm is not only able to detect and land on a given target, but also able to track a target which moves intermittently, and land on it. In [77] [PERSON] _et al._ present a vision-based algorithm designed to enable the AAV to land on a  
Figure 8: These figures are taken from [21]. (a) shows the target-based AUV navigation scheme. Normal AUV visual target tracking systems are developed based on this scheme. (b) shows the components of an underwater image system. moving target. The AAV is required to identify a target, track it, and land on it while the target is also in motion. They use Hu's moments of inertia for the precise target recognition and a Kalman Filter for target tracking. Based on the output of the tracker, a simple trajectory controller is implemented which (within the given constraints) ensures that the AAV is able to land on the target. In [78] it has been shown that Autonomous Aerial Vehicles equipped with a 2-degree-of-freedom pan-tilt camera can be used for the long-term autonomous observation of stationary ground targets. The environmental aspects such as wind and sunlight have an influence on the quality of the images taken by the camera. These influences can set limits to the time span of continuous target observation, especially when the range of camera motion is mechanically limited. Two different camera limit specifications with the realistic applicability have been analyzed for these potential problems.