Online Deep Learning for Improved Trajectory Tracking of Unmanned Aerial Vehicles Using Expert Knowledge

This work presents an online learning-based control method for improved trajectory tracking of unmanned aerial vehicles using both deep learning and expert knowledge. The proposed method does not require the exact model of the system to be controlled, and it is robust against variations in system dynamics as well as operational uncertainties. The learning is divided into two phases: offline (pre-)training and online (post-)training. In the former, a conventional controller performs a set of trajectories and, based on the input-output dataset, the deep neural network (DNN)-based controller is trained. In the latter, the trained DNN, which mimics the conventional controller, controls the system. Unlike the existing papers in the literature, the network is still being trained for different sets of trajectories which are not used in the training phase of DNN. Thanks to the rule-base, which contains the expert knowledge, the proposed framework learns the system dynamics and operational uncertainties in real-time. The experimental results show that the proposed online learning-based approach gives better trajectory tracking performance when compared to the only offline trained network.Comment: corrected version accepted for ICRA 201

Data Sharing based on Facial Recognition Clusters

The evolution of computer vision technologies has led to the emergence of novel applications across various sectors, with face detection and recognition systems taking center stage. In this research paper, we present a comprehensive examination and implementation of a face detection project that harnesses the cutting-edge face recognition model. Our primary aim is to create a reliable and effective system that can be seamlessly integrated into functions allowing users to input their image to capture their facial features, subsequently retrieving all images linked to their identity from a database. Our strategy capitalizes on the dlib library and its face recognition model, which com- bines advanced deep learning methods with traditional computer vision techniques to attain highly accurate face detection and recognition. The essential elements of our system encompass face detection, face recognition, and image retrieval. Initially, we employ the face recognition model to detect and pinpoint faces within the captured image. Following that, we employ facial landmarks and feature embeddings to recognize and match the detected face with entries in a database. Finally, we retrieve and present all images connected to the recognized individual. To validate the effectiveness of our system, we conducted extensive experiments on a diverse dataset that encompasses various lighting conditions, poses, and facial expressions. Our findings demonstrate exceptional accuracy and efficiency in both face detection and recognition, rendering our approach suitable for real-world applications. We envision a broad spectrum of potential applications for our system, including access control, event management, and personal media organization