17 accepted papers
This paper presents a formation control approach for contactless gesture-based Human-Swarm Interaction (HSI) between a team of multi-rotor Unmanned Aerial Vehicles (UAVs) and a human worker. The approach is designed to monitor the safety of human workers, particularly those operating at heights. In
Industrial pipeline inspection in petrochemical refineries is dangerous, expensive, time-consuming and prone to errors. Anomaly detection can play a crucial role towards its automation. Damages in this type of infrastructure are few and can be considered as anomalies (essentially outliers). This pap…
This paper presents a unified system tailored for autonomous pipe segmentation within an industrial setting. To this end, it is designed to analyze RGB images captured by Unmanned Aerial Vehicle (UAV)-mounted cameras to predict binary pipe segmentation maps. The overall proposed system consists of t…
Public opinion measurement through polling is a classical political analysis task, e.g. for predicting national and local election results. However, polls are expensive to run and their results may be biased primarily due to improper population sampling. In this paper, we propose two innovative meth…
Non-Maximum Suppression (NMS) is a post-processing step in almost every visual object detector, tasked with rapidly pruning the number of overlapping detected candidate rectangular Regions-of-Interest (RoIs) and replacing them with a single, more spatially accurate detection (in pixel coordinates).…
This work examines the problem of increasing the robustness of deep neural network-based image classification systems to adversarial attacks, without changing the neural architecture or employ adversarial examples in the learning process. We attribute their famous lack of robustness to the geometric…
This paper presents a novel neural module for enhancing existing fast and lightweight 2D human pose estimation CNNs, in order to increase their accuracy. A baseline stem CNN is augmented by a collateral module, which is tasked to encode global spatial and semantic information and provide it to the s…
Most unsupervised Deep Neural Networks (DNNs) for video summarization rely on adversarial learning, autoencoding and training without utilizing any ground-truth summary. In several cases, the Convolutional Neural Network (CNN)-derived video frame representations are sequentially fed to a Long Short-…
Camera-equipped UAVs, or drones, are increasingly employed in a wide range of applications. Thus, ensuring their safe flight in areas containing people is a top priority. In this paper, a deep neural network-based method is proposed for the task of visual human crowd detection from UAV footage, allo…
This letter proposes three-filters-to-normal (3F2N), an accurate and ultrafast surface normal estimator (SNE), which is designed for structured range sensor data, e.g., depth/disparity images. 3F2N SNE computes surface normals by simply performing three filtering operations (two image gradient filte
Visual Object Tracking remains an open and challenging task in the Computer Vision field, requiring tracking algorithms to achieve a feeble balance between precision and speed performance. In this work, inspired by the classic Mean Shift algorithm for object tracking using histograms, as well as the…
Aerial cinematography relying on camera-equipped umanned aerial vehicles (UAVs), or drones, has revolutionized media production during the past years. Autonomous UAV function-alities are already being employed to a degree, in a manner structured mainly around visual target tracking. From a cinematog…
Storage, browsing and analysis of human activity videos can be significantly facilitated by automated video summarization. Unsupervised key-frame extraction remains the most widely applicable technique for summarizing activity videos. However, their specific properties make the problem difficult to…
Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) salien…
In this paper, we propose an optimization scheme aiming at optimal nonlinear data projection, in terms of Fisher ratio maximization. To this end, we formulate an iterative optimization scheme consisting of two processing steps: optimal data projection calculation and optimal class representation det…
In this paper, we propose a method for video summarization based on human activity description. We formulate this problem as the one of automatic video segment selection based on a learning process that employs salient video segment paradigms. For this one-class classification problem, we introduce…