## 2 Vision based Target Tracking for Autonomous Vehicles Navigation
### Visual Target Tracking for Autonomous Land Vehicles Navigation
In [49] [PERSON] _et al._ present a simple linear method for localizing an indoor mobile robot based on a natural landmark model and a robust tracking algorithm. The landmark model consists of a set of three or more natural lines such as baselines, door edges and linear edges in tables or chairs to take the advantages of fast landmark detection. The canny operator and Lucas-Kanade algorithm are used for the effectively detecting and tracking of the landmark model. Next, a quick localization method for autonomous vehicles from correspondent lines is proposed by adopting a linear technique. Based on assumptions on indoor environments, a complex nonlinear problem for the \(3D\) pose determination using lines is converted to an iterative linear problem, which makes it possible to apply the proposed algorithm for real-time applications. However, in their system the line detection methods are not quite accurate and robust and with some additional condition constraints. In the most recent work of [46], [PERSON] establishes a mathematical framework to integrate SLAM and moving object tracking (Figure 5). He describes two solutions: SLAM with generic objects (GO), and SLAM with detection and tracking of moving objects (DATMO). SLAM with GO calculates a joint posterior over all generic objects and the robot. Such an approach is similar to existing SLAM algorithms, but with additional structures to allow for motion modeling of the generic objects. Unfortunately, it is computationally demanding and infeasible. Consequently, he provides the second solution, SLAM with DATMO, in which the estimation problem is decomposed into two separate estimators. By maintaining separate posteriors for the stationary objects and the moving objects, the resulting estimation problems are much lower dimensional than SLAM with GO. Normally autonomous vehicle is assumed to move in the environment with the prior knowledge of its location. The SLAM and autonomous vehicles target tracking are considered separately [10]. However it is believed by many that a solution to the SLAMMOT problem would expand autonomous vehicles applications in proximity to human beings where autonomous vehicles work not only for people but also with people. From the reviews in this part, the SLAM can be successfully integrated with visual target tracking methods to make autonomous vehicles work at high speeds under situations like the large crowded city urban environment [50]. As a future work, in order to make the vehicle fully autonomously navigate the environment, the SLAM problem can be considered more and more at the same time with the visual target tracking problem. Figure 5: These two figures are taken from [46]. (a) shows the relationship between SLAM and DATMO. The simultaneous localization, mapping and moving object tracking problem aims to tackle the SLAM problem and the DATMO problem at once. Because SLAM provides more accurate pose estimates and a surrounding map, a wide variety of moving objects are detected using the surrounding map without using any predefined features or appearances, and tracking is performed reliably with accurate autonomous vehicles pose estimates. SLAM can be more accurate because moving objects are filtered out of the SLAM process thanks to the moving object location prediction from DATMO. SLAM and DATMO are mutually beneficial. The left of (b) shows the Navlab11 testbed, which is used to test their Simultaneous Localization, Mapping and Moving Object Tracking algorithm. The right of (b) shows the sensors used for the testbed (SICK LMS221, SICK LMS291 and the tri-camera system). Visual Target Tracking with Pan-Tilt Camera Platforms in Autonomous Land Vehicles  
Ego-motion estimation or pan-tilt cameras' motion control are one of the key issues in autonomous vehicles navigation, especially in demand for moving objects tracking. In [51] [PERSON] _et al._ develop a lightweight, robust real-time tracking system used on an experimental geo-referenced cameras platform.