## 2 Vision based Target Tracking for Autonomous Vehicles Navigation
### Visual Target Tracking for Autonomous Land Vehicles Navigation
The purpose of their system is to study the benefits of combining image processing with navigation data that should be available from the control system of any AGV system. Their experiments show that by using a Kalman Filter the tracking algorithm can handle objects' large movements between images and it becomes more resistant to the occlusions. However, their tracking system is designed mainly based on the image brightness, which gives much less robust performance under environmental lighting changes. The objective of the research work in [52] is to derive the orientation of a pan-tilt camera fitting a drone in order to track a target and to maintain its position in the middle of the image (Figure 6). To ensure real-time video operation, an algorithmic solution integrating a successive-step and multi-blocksearch method is implemented, thus allowing tracking with complex target displacements (Figure 6). The micro-controller uses this information to manage the camera orientation. With a certain regularity in the evolution of the target model, this system is sufficiently robust to track deformable targets in real images. However their technique has limitations when the target is close to the cameras. Also only a simple linear interpolation method is carried out. The drone localization and attitude are not considered in the algorithm. In addition, the implementation of a \(2D\) visual camera control requires a priori knowledge of the \(3D\) target model. [PERSON] _et al._[53] present a method of planning a path on which the autonomous vehicle with a pan-tile camera can find the target objects under spatial uncertainties. The object recognition is normally based on feature matching between models and the image data. However there are several problems in this method. For example, in the case that the salient features to identify the target object may center on their particular faces, the autonomous vehicle has to move around the object to find the features. If the possible locations of the target object are in a wide area, the robot has to move around the area to search the object. This paper addresses these problems by a probabilistic approach. Given an initial roughly-planned path, the proposed method optimizes it with respect to the travel time, high pass-ability, and a high probability of finding the target. The method defines a path evaluation function based on these factors and finds a suboptimal path by solving the nonlinear optimization problem of the path evaluation function. Their method combines the visibility constraint and the conventional constraints of travel time and collision avoidance for autonomous vehicles navigation. [PERSON] _et al._[54] develop a pan-tilt visual tracking system to dynamically track moving targets using vision-based control. The algorithm includes the color-based segmentation, data pre-processing and active parameters adjustment. Since computationally expensive techniques are inapplicable, they focus on the use of specified color properties to identify the objects of interest. One of the major limitations in the pan-tilt cameras visual tracking system is that the cameras's movements are rather complex with pan and tilt motions. In the above algorithms, the cameras' complex motions are neglected or not considered enough in an accurate way. If the cameras' self motion can not be precisely identified and integrated into the tracking system, then the visual target tracking performance will not reach a satisfactory level. Visual Target Tracking for Multiple Mobile Robots Cooperation  
Multiple mobile robots (autonomous vehicles) cooperation means that each mobile robot plans its path based on other robots' navigation information. The robots cooperate with each other to complete navigation tasks. Several methods based on the visual target tracking have been proposed in this area. In [55] a real-time visual tracking algorithm for MRFS (Multiple Robot Fishes cooperation System) is described.