## 6 Conclusions and Perspectives
### Limitations and Challenges  
Despite varied and interesting proposals of robust and accurate systems, several challenges and perspectives must be studied and addressed. They are mainly related to the following points (as further discussed in this section):  
* most of the reviewed systems fail to become fully operational, and reasons for this must be analyzed;
* human factors are very important for BVIP, and they must be carefully understood and taken into account, raising challenges for user-centered AI approaches;
* the use of embedded devices is mandatory, but it raises important constraints on AI-based techniques, due to limited storage and computational resources, and to higher power consumption. In addition, incrementality is becoming important for BVIP, as a way to adapt object recognition to specific objects, including personal ones;
* in comparison with other general fields, the available image datasets or pre-trained CNNs specific to BVIP are not widely accessible (or even defined). The adaptation of such models to specific conditions (cultural, dynamic, etc.) is difficult. In most of the studied papers, the presented prototypes are not yet ready to be deployed. This difficulty in achieving the last step of development is caused by several factors that will have to be solved, with the lack of complete and available solutions representing an additional difficulty for BVIP. First, despite the continuing innovations in the field of AI and computer vision, researchers still face serious issues when developing navigation tools for BVIP. Many situations can be challenging for navigation systems:  
* obstacles situated at ground or head levels are still difficult to detect, and those are very important for BVIP;
* many elements present in urban environments are geographically or culturally dependent (for example, New York yellow cabs are quite different from London black ones);* appropriate scene description is quite subjective (what information is really important depends on the user and the context). These issues are due to the huge complexity of the problems, but also to the fact that some research fields have been more developed than others in recent years, and not for the specific case of BVIP. For example, object detection and outdoor localization are much more advanced today than safe street crossing or environment mapping. Salient object detection was once a major issue when developing assistive technologies for BVIP, but is now about to be overcome thanks to recent innovation, such as that described by [PERSON] et al. in [51]. In addition, it might seem a minor point, but one should note that most existing CNNs available for use are trained with images of objects or scenes that are not specific to BVIP. Many relevant objects or situations (for BVIP) are missing from training sets. Building such training sets and specific CNNs, and making them available to the scientific (and developer) community, would certainly help expand AI for BVIP. Additionally, human factors are very important in such AI applications. There is often a gap between end-user needs and developer decisions. As shown in this paper, BVIP are not always engaged in the final testing phase, and even more rarely in the engineering process. Some important requirements often remain unfulfilled, thus leading users to abandoning the developed systems. Including BVIP as well as health professionals in the development process of AI-based systems is certainly necessary to increase the chance of those systems becoming operational. More generally, the human factors involved in such specific AI applications play a crucial role, and are part of user-centered AI approaches. Taking into account such factors raises challenging issues. For instance, the design of human-machine interfaces does not always take into account the user relationships to their environment. For example, many BVIP do not wish to walk around with smartphones in their hand, fearing dropping them or being robbed.