# Distance Assessment by Object Detection--For Visually Impaired Assistive Mechatronic System
## 3 Materials and Methods
### Low-Cost Camera Subsystem  
In this subchapter is presented a new technique for distance assessment based on using special signs. The signs designed to mark special areas are presented in Figure 3. For object detection was used a low-cost stereoscopic camera included in the mobile phone HTC3D EVO with a maximum resolution of 1920 \(\times\) 1080 pixels for the images captured.  
When determining distances to objects, one of the methods is to recognize a certain mark (sign, symbol, etc.) or object with a camera, and then to determine the distance up to it. This is based on determining the disparity between the reference images stored in a database and those collected by the camera. This method increases the disparity, highlighting specific areas with well-highlighted and limited colors.  
A successful example can be seen in Figure 4. The reference points on the sign border are well highlighted in both the reference image and the actual image.  
Figure 3: Initial sign types proposed for the study: (**a**) signs with geometrical representation; (**b**) signs with hatch representation.  
The signs can be constructed using stickers on various objects or in areas of interest that have clear, easily recognizable, and specially designed geometric shapes that are easy to understand: circle, square, rhombus, triangle, straight lines, etc. Moreover, these signs can also contain elements for evidencing using coloristics (different colors, different shades), as well as combinations of all of these elements.  
People with visual impairments who move from one place to another are referred to in this article, in a friendly way, as \"Explorers\".  
Some of the signs we propose are shown in Figure 4. Rich colors and simple geometric figures are preferred, both for good recognition and for the positive effect on the \"Explorers\". The number of features detected in the images improves the accuracy of the object detection. This number increases when hatches are used in the signs. However, this method does not provide the best results when the object detection is based on disparity assessment. OCR techniques based on image vectorization are more appropriate for the recognition of signs with hatches on the area inside [45].  
The determination of distances to objects is based on the existence of these signs on objects or in special areas (as in the second method of this study). If the size of the signs is predetermined, the correspondence curve between the size of the object in the captured image and its distance to the object can be determined experimentally. Such a curve is shown in Figure 5a for a stereoscopic 3 DEvo camera with 1920 \(\times\) 1080 pixel image resolution and a 33 mm distance between the cameras, while the experimental results are evidenced in Figure 6.  
Figure 4: A successful recognition example showing all matches. (a) Features on real image; (b) features on reference image.  
Figure 5: Correspondence curve between object size and distance to object for low-cost cameras. (a) Calibration curve fit; (b) low-cost cameras (authors’ mobile phones).