# Scene Description for Visually Impaired People with Multi-Label Convolutional SVM Networks
## 1 Introduction  
Chronic blindness may occur as an eventual result of various causes, such as cataract, glaucoma, age-related macular degeneration, corneal opacities, diabetic retinopathy, trachoma, and eye conditions in children (e.g., caused by vitamin A deficiency) [1]. Recent factsheets from the World Health Organization, as per October 2018 [1], indicate that 1.3 billion people suffer from some form of vision impairment, including 36 million people who are considered blind. These facts highlight an urgent need to improve the quality of life for people with vision disability, or at least to lessen its consequences. Towards achieving the earlier endeavor, assistive technology ought to exert an essential role. On this point, the latest advances gave rise to several designs and prototypes, which can be regarded from two distinct but complementary perspectives, namely (1) assistive mobility and obstacle avoidance, and (2) object perception and recognition. The first perspective enables the visually impaired (VI) persons to navigate more independently, while the latter emphasizes consolidating their comprehension of the nature of the nearby objects, if any. Navigation-focused technology constitutes the bulk of the literature. Many works have been carried out making use of ultrasonic sensors to probe the existence of nearby obstacles via transmitting and subsequently receiving ultrasonic waves. The time consumed during this process is commonlytermed time of flight (TOF). In [2] for instance, a prototype that consists of a guide cane, around a housing, a wheelbase, and a handle is presented. The housing is surrounded by ten ultrasonic sensors, eight of which are spaced by \(15^{\circ}\) and set up on the frontal part, and the other two sensors are placed on the edge to sense lateral objects. The user can use a mini joystick to control the preferred direction and maneuver the cane in order to inspect the area. If an obstacle is sensed, an obstacle avoidance algorithm (installed on a computer) estimates an alternative obstacle-free path and steers the cane through by applying a gentle force felt by the user on the handle. A similar concept was suggested in [3]. Another prototype, which consists of smart clothing equipped with a microcontroller, ultrasonic sensors, and alerting vibrators was proposed in [4]. The sensors are adopted to detect potential obstacles, whilst a neuro-fuzzy-based controller estimates the obstacle's position (left, right, and front) and indicates navigation tips such as 'turn left' or 'turn right'. A similar concept was presented in [5]. Another ultrasonic-based model was designed in [6]. The common downsides of the prototypes discussed earlier are their size and power consumption render them impractical for daily use by a VI individual. Alternative navigation models suggest the use of the Global Positioning System (GPS) to determine the location of the VI users to instruct them towards a predefined destination [7]. These latter models, however, can be accurate to determine the location of the user but are unable to tackle the issue of obstacle avoidance. Regarding the recognition aspect, the literature reports some contributions, which are mostly based on computer vision. In [8], a banknote recognition system for the VI, which makes use of speeded-up robust features (SURF), was presented. [PERSON] et al. [9] proposed a supported supermarket shopping design, which considers a camera-based product recognition by means of QR codes that are placed on the shelves. Another product barcode detection, as well as reading, was developed in [10]. In [11] another travel assistant was proposed. It considers the text zones displayed on public transportation buses (at bus stops) to extract information related to the bus line number. The proposed prototype elaborates a given image (acquired by a portable camera) and subsequently notifies by voice the outcome to the user. In another work [12], assisted indoor staircases detection (within 1-5 m ahead) was suggested.