2/19/2023 0 Comments Nexuiz youtube![]() Using a graphical tool, the user can design a set of hand poses (such as victory, fist, palm, etc.), train the system by showing it the poses and finally get a working classification system that can be integrated into any C++ application. I then developed a depth-data-based hand pose training and recognition system. The goal to maximize the interaction space beyond 1.5 m with the Kinect turned out to be impossible. I then explored the use of skin-color classification from the high-resolution RGB image, but all attempts showed major deficiencies in real-world environments. I initially analyzed the ability to track hands and individual fingers at large distances ( paper), but found that finger-tips (and more generally, hand poses) are hard to identify at distances beyond 1.5 m between hand and sensor, due to limited depth image resolution. The last project was my Master thesis, with the topic “Building an appearance-based hand gesture recognition system using the Microsoft Kinect”. Student projects Master thesis: hand gesture recognition systemįrom 2011 – 2012 I worked as student at Fraunhofer FIT, before becoming a full-time employee. ![]() These two videos give you a better idea of the system in action. To use a video see-through approach may seem odd, but it had (and still has) several advantages, such as a much larger FOV (compared to optical see-through AR headsets) and centimeter-precision location data, which even today’s devices cannot deliver. The VR goggles are connected to a laptop workstation that performs image and sensor data processing (including sensor fusion), and records all data streams to disk, for a later replay, back in the office. Thanks to centimeter-precision sensors, the viewer gets an accurate, real-time preview of where the building is located and how it fits into the context of the surrounding area. This otherwise empty construction site is superimposed with a virtual model of the building. Through Virtual Reality goggles, the co-driver sees a real-time visualization of the real world, as recorded by a 360° panoramic camera. Targeting users with some sensors such as location is still possible, but with reduced resolution and increased delay.įrom 2012 to 2013 I developed the AutoAR prototype ( German website), an Augmented Reality system mounted on a car’s roof that visualizes building models which are planned to be erected at a specific location in the real world. To conserve power and prolong battery life, most sensors can no longer be continuously queried in the background. ![]() While implementing such apps was already challenging back in 2014, mobile OS’s have adapted by now. A special indoor-version of the app heavily relied on Bluetooth Low Energy (BLE) beacons which were pre-installed in all rooms of the volunteer’s homes to determine their location. if the all of the conditions “entered the central station geo-fence” + “it is a week day” + “time is between 7-9 AM” + “user’s activity is ‘standing still’ since one minute” hold – the conjecture being that the user is likely waiting for a train or bus. This allowed to show specific messages for a certain context, e.g. The input values are matched against a set of contexts, which are regularly retrieved from a server. standing still, display state, ringer volume settings, proximity, or the current time and date. current location, activity such as walking vs. I implemented a context matching engine for Android that collects various inputs, e.g. The app would continuously run in the background on the phones of volunteers who gave their consent (mostly students and employees of our client). ![]() In 2014 I helped develop a smartphone app internally called “Mobile360” that was used in an experiment to determine whether end-users would accept contextualized advertisements presented via push notifications. In addition, BSync’s architecture abstracts the file system specifics of different cloud storages, which allows to add support for new providers with little effort. A designated part of the user interface shows a log of conflicts to the user and explains them in simple terms, to facilitate the recovery process. BSync will always propagate the detected operations without side effects, avoid conflicts whenever possible, and notify users about real conflicts which BSync solves preliminarily. This allows many operations to accumulate or conflict. ![]() It has better support for users who work offline for extended time periods. I apply elaborate algorithms to solve edge cases most other tools don’t care about (but really should). In contrast to similar tools like the Dropbox or OneDrive client, BSync was created from an academic background, as part of my PhD dissertation. BSync is a consumer-oriented file synchronizer for macOS and Windows that keeps several local folders synchronized with a cloud storage. ![]()
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