# Scene Description for Visually Impaired People with Multi-Label Convolutional SVM Networks
## 3 Experimental Results  
### Dataset Description  
In the experiments, we evaluate the proposed method on three datasets taken by a portable camera mounted on a lightweight shield worn by the user, and connected via a USB wire to a laptop processing unit. This system incorporates navigation and recognition modules. In a first step, the user runs the application to load the offline-stored information related to recognition and navigation. Then he can control this system using verbal commands as shown in Figure 5a. For the sake of clarity, we provide also a general view of the application, where the user asks to go to the 'elevator'. Upon the arrival to the desired destination using a path planning module, the prototype notifies the user that the destination is reached. Figure 5b shows the current view of the camera, where the destination (elevators) is displayed. The system also features a virtual environment emulating the real movement of the user within the indoor space. As can be seen, the user is symbolized by the black top-silhouette, emitting two lines, the blue line refers to the user's current frontal view; the green point refers to the destination estimated by the path planning module; while the red dot highlights the final destination. The interface displays also markers displayed as thick lines laying on the walls for helping in the localization. In our work, we have used this system to acquire different images used for developing the recognition module based on M-CSVM. The first and second datasets acquired by this system are composed of images of size \(320\times 240\) pixels. Both datasets have been taken at two different indoor spaces of the faculty of science of University of Trento (Italy). The first dataset contains 58 training and 72 testing images, whereas the second dataset contains 61 training images and 70 testing images. On the other the side, the third dataset is related to outdoor environment and was acquired over different locations across the city  
Figure 5: Overview of the system developed for visually impaired (VI) people. (**a**) Camera mounted on the chest, tablet, and headphones for voice communication; and (**b**) graphical interface. of Trento. The locations were selected based on their importance as well as the density of people frequenting them. The dataset comprises two hundred (200) images of size \(275\times 175\) pixels, which were equally divided into 100 training and testing images, respectively. It is noteworthy that the training images for all datasets were selected in such a way as to cover all the predefined objects in the considered indoor and outdoor environments. To this end, we have selected the objects deemed to be the most important ones in the considered spaces. Regarding the first dataset, 15 objects were considered as follows: 'External Window', 'Board', 'Table', 'External Door', 'Stair Door', 'Access Control Reader', 'Office', 'Pillar', 'Display Screen', 'People', 'ATM', 'Chairs', 'Bins', 'Internal Door', and 'Elevator'. Whereas, for the second set, the list was the following: 'Stairs', 'Heater', 'Corridor', 'Board', 'Laboratories', 'Bins', 'Office', 'People', 'Pillar', 'Elevator', 'Reception', 'Chairs', 'Self Service', 'External Door', and 'Display Screen'. Finally, for the last dataset, a total of 26 objects were defined as follows: 'People', 'Building', 'Bar(s)', 'Monument(s)', 'Chairs/Benches', 'Green ground', 'Vehicle(s)', 'Stairs', 'Walk path/Sidewalk', 'Fence/Wall', 'Tree(s)/Plant(s)', 'Garbage can(s)', 'Bus stop', 'Crosswalk', 'River', 'Roundabout', 'Pole(s)/Pillar(s)', 'Shop(s)', 'Supermarket(s)', 'Pound/Birds', 'Underpass', 'Bridge', 'Railroad', 'Admiration building', 'Church', and 'Traffic signs'. Figure 6 shows sample images from each dataset.