## 4 Remaining Research Challenges and Future Research Directions
A tracker that takes advantage of contextual information to incorporate general constraints on the shape and motion of objects will usually perform better than one that does not exploit this information. 3. Most of tracking algorithms require off-line training information about the target and/or the background. Such information is not always available. Moreover, as the object appearance or background varies, the discriminative features also vary. Thus, there is a need for online selection of discriminative features. 4. In a similar vein, most tracking algorithms use pre-specified models for object representation. The capability to learn object models online will greatly increase the applicability of a tracker. 5. Among different probabilistic state-space methods including Kalman Filters, Joint Probabilistic Data Association Filter (JPDAFs), Hidden Markov Models (HMMs), and Dynamic Bayesian Networks (DBNs), DBNs are probably the most general method for representation of conditional dependencies between multiple variables and/or image observations. However there is a need for more efficient solutions for inference before DBNs are more commonly used in tracking applications. Sixthly, for normal data fusion algorithms, all the sensors data can not arrive at the same time and different sensors have different data capturing and processing time. As a result, the fusion algorithm receives different measurements in different time stamps. There is the Out-Of-Sequence Measurements \"OOSM\" (or called delayed measurements) problem [88]. In [PERSON]'s paper [88], it is also known as the \"negative-time measurement update\" problem. In the normal multi-sensor fusion based target tracking systems, the OOSM problem is often ignored or just simply solved by using some human reasoning methods like [89]. In order to make the accurate target state estimation, this Out-Of-Sequence Measurements (OOSM) problem must be solved. Since there are the above remaining challenges, future work must be investigated before being able to establishing a system that produces quality visual target tracking results while overcoming all the challenges implicated by operation in the real-time environment. Following research directions are suggested for future further considerations:  
* To make more general applications in different environments under both known or unknown situations, the target tracking scheme which only rely on the autonomous vehicle's onboard sensors can be developed as a better solution to detect and track the object's \(3D\) dynamics. * A dynamically changing background is considered in visual target tracking for autonomous vehicles navigation. The robust target tracking system needs to be developed in the MCMO state. * Instead of using the target's dynamics relative to the cameras, a tracking algorithm which can accurately derive the target's \(3D\) dynamic information in the world coordinate can be developed to give more applicable and accurate target tracking results. * At present, many tracking algorithms are achieved by placing a specific visual property on the objects of interest. The limitation is mainly due to the finite object features or the models information. The research aiming at tracking the targets without placing any special property on the objects of interest can be considered for autonomous vehicles navigation tasks. * In a MCMO state, the visual features of the object and the background are changing continuously. It is necessary to make the system have the ability to adapt to these changes, which means that the tracking algorithms should be developed having some learning or adaptive estimation strategies. * The ideas of data fusion can be more thoroughly studied and applied in visual target tracking for autonomous vehicles navigation. With successful data fusion, the tracking system can easily and accurately achieve the tracking requirements for autonomous vehicles navigation. * In order to accurately track the object's \(3D\) dynamics in different situations, there are several well-known tracking problems to be solved.