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. The tracking engine tracks the homography changes using optical flow algorithm and then refines the residual warp using the optimized template matcher.
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. This is a public dataset containing ten classes of vehicles with different orientations with 0.3mx0.3m.
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). We have developed the positioning algorithm based on cheap Bluetooth beacons and built-in IMU sensors on mobile device.
It is known, that nowadays almost every indoor positioning and navigation system (IPNS) consists of a radio signals part (Wi-Fi or BLE) and a part based on smartphone inertial sensors. Both parts contain a number of challenges complicating a precise user positioning using mobile phones or tablets. In the paper, we describe several contributions. Firstly, a problem of BLE packets recovering is considered.
In the paper, two examples of embedded vision modules are described. Firstly, it is demonstrated how fiducial marker tracking algorithm can be adopted for operation on Raspberry Pi. Usage of proposed ideas allows to achieve around 60fps speed of binary marker tracking. Secondly, we describe the problem of text detection and recognition in outdoor environment. Experimental results indicate on acceptable results and good potential to provide low-cost and efficient embedded vision system for this purpose.
The competition on the world market of smartphones and tablets between the acknowledged leaders on one side and the numerous newcomers on the other makes them all look for new solutions that open additional opportunities for the developers and customers without the growth of the price. The progress with new opportunities turns possible when it happens simultaneously in the software and the hardware.