We are looking for an experienced and dedicated software engineer with a strong mathematical background, ability to solve challenging technical problems in image processing and computer vision. Key responsibilities are development of image and signal processing algorithms, reading and implementation of state-of-the-art papers, R&D activities. Main research directions are pattern recognition, object tracking, detection and recognition.
We are looking for an experienced and dedicated data scientist with a strong mathematical background, ability to solve challenging technical problems using machine learning and deep learning in image and video processing domains.
In the paper, we describe technical details of multi-player sports tracker system. We demonstrate that proper in-depth analysis of video frames sequence may provide a lot of useful information required for sports analytics. Object detection and tracking steps are analyzed. Novel ideas for efficient filtering of false detections and irrelevant tracks are proposed.
3D mapping techniques have a large variety of applications from entertainment to military and medical fields. However, there is a big challenge of obtaining well refined 3D model from a set of images without usage of depth sensors.
Indoor positioning systems in GPS-denied environments are rapidly becoming popular. Various options are commonly available (BLE, Wi-Fi, ultra-wideband, ultrasonic, etc.). The key challenge is to provide accurate, and stable real-time user location at a low cost. In this paper, we present the research and production details of the developed hybrid indoor localization and navigation system (HILN).
Automatic document analysis and recognition is a hot topic in a modern computer vision. A common scenario is when the user takes a picture by mobile phone or tablet and the goal is to automatically parse and recognize content from the captured document. Such like pictures, tables, text data, links, etc.
Marker-based augmented reality (AR) is extremely popular nowadays. However, full user immersion is possible only in the case of robust real-time computer vision solutions working on the mobile device. We have developed a custom hybrid tracking system based on local feature tracking and template-based matching.
Object recognition is an important computer vision and machine learning problem. A particular case is automatic target recognition (ATR) on radar images. In the project, our team has developed a custom classification algorithm based on two different tools. We have been working with MSTAR dataset.
Fiducial markers are widely used in various applications like robot navigation, logistics, augmented reality. Fig. 1. Applications of fiducial markers Advantages are obvious High contrast Simple code generation Resistance to extremal angles However, when we deal with a large number of markers, real-time recognition becomes challenging, especially on embedded devices with low power CPUs on-board.
Indoor positioning systems are becoming popular nowadays. Indeed, there is plenty of opportunities for real-time user navigation in GPS-denied environments. An interesting use cases are as follows: Fig. 1. Indoor navigation use cases There are several options for hardware (see It-Jim blog post).