## 3 Fusion based Visual Target Tracking for Autonomous Vehicles Navigation  
Since each navigation method has its own merits and demerits, it is necessary to have a fusion methodology to combine their advantages and also compensate for their disadvantages. Also autonomous vehicles are equipped with different sensors (such as inertial motion sensors, camera, laser scan, sonar, radar, GPS and so on). The sensors data are redundant and complementary. Fusing these information can lead to a robust feature extraction and target tracking performance. Therefore in this section the general methods of sensor data fusion for visual target tracking by autonomous vehicles are reviewed. In Table.1 [PERSON] _et al._[79] review the general data fusion methodologies. Data fusion algorithms can be broadly classified as follows: estimation methods, classification methods, inference methods, and artificial intelligence methods. Data fusion has been widely used in visual target tracking for autonomous vehicles navigation. In the article of [80], a multi-sensor fusion approach between an omnidirectional vision system and a panoramic range finder is presented to dynamically localize an autonomous vehicle. These two sensors provide some complementary and redundant data which enable to construct a robust sensorial model which integrates an important number of significant primitives. Based on this model, they treat the problem of maintaining a matching and propagating uncertainties on each matched primitive. In details their localization method is based on the tracking of significant landmarks of the environment and integrating a multi-level uncertainty propagation stage based on the usage of the Dempster-Shafer theory. For \begin{table}
\begin{tabular}{|l|l|} \hline Estimation methods & Non-recursive: \\  & *Weighted Average \\  & *Least Squares \\  & Recursive: \\  & *Kalman Filter \\  & *Extended Kalman Filtering \\ \hline Classification methods & *Parametric Templates \\  & *Cluster Analysis \\  & *Learning Vector Quantization (LVQ) \\  & *K-means Clustering \\  & *Kohonen Feature Map \\  & *ART, ARTMAP, Fuzzy-ART Network \\ \hline Inference methods & *Bayesian Inference \\  & *Dempster-Shafer Method \\  & *Generalized Evidence Processing \\ \hline Artificial intelligence methods & *Expert System \\  & *Adaptive Neural Network \\  & *Fuzzy Logic \\ \hline \end{tabular}
\end{table} 
Table 1: The Classification of Data Fusion Algorithms from [79]. this purpose, they have developed a robust multi-criteria method which solves two problems linked to the tracking: the propagation of an uncertainty concerning the landmark tracks and the treatment of the apparition and momentary disappearance of a track. In [81] [PERSON] _et al._ present a new hybrid approach for range estimation that combines inertial and visual based technologies, which allows them to calculate the image-space distance between the autonomous vehicle and the edge lines of the \(3D\) environment. Two frames obtained from the moving CCD video cameras and the outputs of the inertial tracking system which report the relative changes of orientations and accelerations between the two frames are integrated to estimate the image-space distance of different \(3D\) points. Using the Cellular Nonlinear Network (CNN) tracking method, with a combination of inertial and visual tracking technologies, [PERSON] _et al._[82] have presented a direct trigonometric range estimation method from a stream of images in the static scene for visual target tracking. [PERSON] _et al._[83] describe how to integrate tracking functions on an autonomous vehicle in different situations: landmark tracking to guarantee the real-time robot localization, target tracking for a sensor based motion or obstacle tracking to control an obstacle avoidance procedure. These objects such as landmarks, targets, and obstacles have different characteristics. Moreover, normal tracking methods are not robust enough to work under varying environmental conditions. Tracking often fails when illumination undergoes significant changes.