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The modified You only look once (YOLO) network architecture that allows one-dimensional direction estimation along with classic object detection in real time, is considered in the task of street traffic surveillance from unmanned aerial vehicles. The key feature is a modified output fully connected layer with additional orientational parameters. It has been shown that this network can estimate the direction of vehicles on a custom testing dataset with photos.

AR technology is rapidly growing direction. Nevertheless, mobile and embedded AR still faces a lot of real-life problems and challenges. The talk will be devoted to review of AR principles and applications. The key part will contain the analysis of available technical solutions for mobile AR. Indeed, quite a lot of software engineers use the 3rd part frameworks (ARkit, ARcore, Vuforia, etc.) without putting enough attention into algorithmic and implementation details. In the presentation, we will consider the state-of-the-art algorithms for marker detection and recognition. Moreover, several fresh methods for real-time marker tracking will be discussed. In addition, such hot topics as markerless tracking, sensor fusion and SLAM will be covered as well. Finally, several real demos from custom mobile AR SDK will be shown.

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. A specific version of a Kalman filter for received signal strength indicator (RSSI) data analysis is developed. The proposed modification allows recovering lost data as well as providing sufficient signal smoothing. Secondly, a custom step detection procedure based on an inertial navigation system (INS) is developed. Unlike to a common solution based on the thresholding of linear acceleration amplitude, an advanced version of the detector is highlighted. Finally, a hybrid indoor localization and navigation (HILN) system developed on the basis of a particle filter (PF) and the proposed modifications for BLE and INS parts is described. Experimental results are provided.