# Distance Estimation with a Stereo Camera and Accuracy Determination
## 1 Introduction  
Distance estimation using stereo cameras is one of the key issues in the field of computer vision and robotics, which finds application in various areas of technology and industry. The technology is based on the use of two cameras placed at a certain distance from each other, which capture images of the same scene from different viewing angles. The basic concept behind this method is that the difference between the images captured by the two cameras, known as binocular disparity (binocular parallax), contains information about the depth of the scene. It is the main depth cue that makes stereoscopic images appear three-dimensional. However, in many scenarios, the range of depth that can be reproduced by this signal is severely limited and is usually fixed due to limitations imposed by the displays. For example, due to the low angular resolution of current automultiscope displays, they can only reproduce a small depth range. Analyzing these differences, which is the process of stereoscopy, makes it possible to recreate the three-dimensional structure of the environment and precisely determine the distance to individual objects in the cameras' field of view. This technology is widely used in many fields. In mobile robotics, for example, stereo systems are used for navigation and obstacle avoidance, enabling robots to navigate in unfamiliar and dynamic environments [1; 2; 3]. In the automotive industry, stereo cameras are an integral part of advanced driver assistance systems that enhance road safety through automatic braking, lane keeping, or pedestrian recognition. Also in autonomous vehicles, there are systems to measure the distance between vehicles for autonomous driving based on processing images obtained from stereo cameras [1; 2]. Stereo cameras are also used to monitor the condition of road surfaces, including the detection of ice, water, snow, and dry asphalt. The system is based on the analysis of changes in the polarization of light reflected from the road surface, and the recognition accuracy has been improved through texture analysis, which evaluates the image contrast. Although tests have shown the need for further improvements, especially to adapt the system to changing lighting conditions, the technology is a promising solution to detect road hazards [3]. There are studies of distance measurement using a stereo camera, in which the test subject was a human. The process included steps such as camera calibration, image rectification, disparity calculation, and 3D reconstruction. The distance of the subject was measured using [PERSON]'s distance measurement method to determine the shortest distance from the center of the bounding box to both cameras [4]. Stereoscopic cameras have also been used to develop an inexpensive diagnostic system for ophthalmology that could find application in developing countries. Due to its stereoscopic capabilities and low manufacturing costs, the prototype camera provides clear images of the fundus of the eye, allowing the user to choose between photo and video. This innovative system represents a significant step forward in available medical technology, meeting the needs of professionals working in resource-limited environments [5]. Stereo cameras have also been used to visualize 3D ultrasound data of the breast to better understand the depth and shape of tumors. For this purpose, an acquisition system with automatic ultrasound head travel has been developed that creates a series of parallel image cross sections and then combines them into a 3D model. With a stereoscopic monitor, doctors can view these data in the form of spatial projections, improving the accuracy of cancer diagnosis [6]. To measure the distance of an object from the stereo cameras, it is first necessary to accurately detect the object in the images captured by both cameras. To do this, advanced computer vision techniques, such as Mask R-CNN (Mask Region-Based Convolutional Neural Network), can be used.