## 3 Fusion based Visual Target Tracking for Autonomous Vehicles Navigation
Some other situations where tracking methods fail are due to the cluttered background, changes in the model pose, occlusions of the target image or motion discontinuities in the object dynamics. Thus under the real-world environmental conditions, a single tracking method cannot deal with all the different tasks and situations presented in it. Several methods must be integrated on autonomous vehicles. In their paper, a tracker controller is in charge to activate a specific method with respect to the subject type and a transition model used to recover the tracking failures. This method is validated using four trackers based on: template differences, set of points, lines and snakes. This method requests the tracker to switch, either if a target is lost, by one quick but not so robust method, and a more robust method is activated to find again the target using local image measurements, or if a target switch is necessary, for example when the robot must turn around an edge after a corridor following execution. In such a way every target is associated with the more adapted tracker and with a recovery procedure in case of failures. In the paper of [84] a multiple-sensor multiple-target tracking (MS-MTT) approach for the Autotax system is proposed. The system consists of two basic components: the sensor-level tracking and multiple-sensor track fusion center. Each sensor in the sensor level is considered as an intelligent one which generates its own track files. Thus, the task of the fusion center is to fuse the local track files to produce a more accurate and reliable single system track file. This is performed in three stages: data alignment, track-to-track association and track fusion. In their paper a decentralized sequential data association and fusion structure, referred to as the Sequential Minimum Normalized Distance Nearest Neighbor (SMNDNN) with the majority decision making MDW/OR logic method, has been considered for the Autotaxi application. Another example of the implementation of sensor fusion algorithms for autonomous vehicles target tracking is proposed by [79]. The experimental setup of their system consists of one autonomous vehicle and one multi-sensor based electrical wheelchair. The autonomous vehicle (Nomad \(200\) platform) is a three-wheel mobile platform equipped with a vertical sliding manipulating arm and other sensory modules. The experimental target is the multi-sensor based electrical wheelchair which is developed by them. Their system contains two major agents for the local decisions. One is the target-tracking agent whose inputs are the target position measurements from the fusion of the ultrasonic and vision sensors and the other is the collision-avoidance agent whose inputs are the surrounding range measurements from the fusion of the \(16\) ultrasonic sensors. The final decision calculated by fusion of these two local decisions is the absolute driving velocity of the autonomous vehicle. Most recently [PERSON] _et al._[85; 86] propose a novel data fusion scheme for visual object identification and tracking by autonomous vehicles. In their scheme, image motion vectors fields, color features, visual disparity depth information and cameras motion parameters are fused together to identify the target \(3D\) visual and dynamic features. Their paper also presents a detailed description of the \(3D\) target tracking algorithm using an Extended Kalman Filter with a constant velocity dynamic model. The details of the their proposed system can be seen from Figure 10.  
In conclusion, data fusion improves the system performance significantly and make robust estimations for vision based target tracking by autonomous vehicles. Based on the Table.1 and above reviews, data fusion algorithms can be generally developed in two different ways: (1) fusing data from different sensors or different data from one sensor; (2) integrating different methods. Data fusion shows many promising advantages and should be paid more attentions to in the future research work.