In this paper, we address the problem of lightweight and effective visual object tracking and we present a real-time tracking system suitable for integration in embedded autonomous platforms. We propose a novel tracking framework for classification-based re-detection and tracking, with learnable management of tracking and detection results. The proposed framework includes a novel, very efficient object reidentification method, which filters the detection candidates and systematically corrects the tracking results. In our experiments, we demonstrate the effectiveness of the proposed system by comparing its performance against several other state-of-the art trackers and report the results on the UAV123 and UAV20L datasets. The results indicate that the proposed method is significantly more robust and accurate against recent state-of-the-art trackers, surpassing problems caused by real-world scenarios, while maintaining fast tracking speeds, making it suitable for use in real-time vision applications for autonomous robots, such as Unmanned Aerial Vehicles (UAVs).

Deep learning methods are powerful approaches but often require expensive computations and lead to models of high complexity which need to be trained with large amounts of data. In this paper, we consider the problem of face detection and we propose a light-weight deep convolutional neural network that achieves a state-of-the-art recall rate at the challenging FDDB dataset. Our model is designed with a view to minimize both training and run time and outperforms the convolutional network used in [1] for the same task. Our model consists only of 113.864 free parameters whereas the previously proposed CNN for face detection had 60 million parameters. We propose a new training method that gradually increases the difficulty of both negative and positive examples and has proved to drastically improve training speed and accuracy. Our second approach, involves training a separate deep network to detect individual facial features whilst creating a model that combines the outputs of two different networks. Both methods are able to detect faces under severe occlusion and unconstrained pose variation and meet the difficulties and the large variations of real-world face detection.