We envision that in the near future, humanoid robots would share home space and assist us in our daily and routine activities through object manipulations. One of the fundamental technologies that need to be developed for robots is to enable them to detect objects and recognize them for effective manipulations and take real-time decisions involving those objects. In this paper, we describe a novel architecture that enables multiple low-compute NAO robots to perform real-time detection, recognition and localization of objects in its camera view and take programmable actions based on the detected objects. The proposed algorithm for object detection and localization is an empirical modification of YOLOv3, based on indoor experiments in multiple scenarios, with a smaller weight size and lesser computational requirements. Quantization of the weights and re-adjusting filter sizes and layer arrangements for convolutions improved the inference time for low-resolution images from the robots camera feed. YOLOv3 was chosen after a comparative study of bounding box algorithms was performed with an objective to choose one that strikes the perfect balance among information retention, low inference time and high accuracy for real-time object detection and localization. The architecture also comprises of an effective end-to-end pipeline to feed the real-time frames from the camera feed to the neural net and use its results for guiding the robot with customizable actions corresponding to the detected class labels.
The world is under an ongoing pandemic, COVID-19, of a scale last seen a century ago. Contact tracing is one of the most critical and highly effective tools for containing and breaking the chain of infections especially in the case of infectious respiratory diseases like COVID-19. Thanks to the technological progress in our times, we now have digital mobile applications like the Corona-Warn-App for digital contact tracing. However, due to the invasive nature of contact tracing, it is very important to preserve the privacy of the users. Privacy preservation is important for increasing trust in the app and subsequently enabling its widespread usage in a privacy-valuing population. In this paper, we present a visual simulation of the working of the Corona-Warn-App to demonstrate how the privacy of its users is preserved, how they're notified of infectious contacts and how it helps in containing the spread of COVID-19.
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