Generative One-shot Learning (GOL)

The Generative One-shot Learning (GOL) algorithm is a generative framework which takes as input a single object instance, or generic pattern, and a small set of so-called regularization samples used to drive the generative process. New synthetic data is generated from the single object instance using a set of generalization functions. The proposed system encompasses a Deep Neural Network classifier which gets updated with each data generation iteration. The GOL training procedure follows a multi-objective optimization approach, where a generalization energy, given by the distance between the generated artificial synthetic data and a set of regularization samples, is maximized, while the classification accuracy between each object class is also maximized. Read more

3D Traffic Participants Detection using a Mono-camera

The traffic participants detection algorithm is a component within the more comprehensive road detection system, which is able to detect neighboring vehicles on the road. Using the model obtained by the road detection system, the Traffic Participants Detection algorithm can estimate the "real world" distance between the ego-vehicle and other cars present in the scene. Read more

RGazE: Human-Robot Interaction through Collaborative Tracking

The ROVIS Robust Gaze Estimator (RGazE) is a fast and accurate 3D human gaze estimation algorithm which uses a collaborative tracking framework composed of a cascade of region and spatial classifiers for the extraction of the facial Regions of Interest (ROI), followed by a Gaussian Mixture Model (GMM) point estimator for calculating the facial feature points. One key concept behind this work is to control the parameters of the classifiers with respect to a feedback variable describing the quality of feature extraction. Read more

The ROVIS Human-Robot Interaction and Tracking System

First experiments with the ROVIS human-robot interaction and tracking system on a Neobotix MP 500 mobile platform. Performance evaluation at the Department of Information Technology, Széchenyi István University, Gyor, Hungary

Generic Fitted Primitives

The purpose of a 3D Generic Fitted Primitive (GFP) to fully reconstruct 3D object from sparse visual data. A modelling step is used to particularize the obtained primitive volume with the purpose of determining safe and robust grasp actions in service robotics. Read more

3D Multiple Objects Tracking

The 2D-3D Collaborative Tracking (23CT) method for tracking rigid bodies in the context of mobile robotic manipulation is illustrated in this video. The tracking approach is based on a collaborative tracking framework developed around a 2D multi-class Region of Interest tracking system and a 3D model-based tracker, where both trackers benefit from each others results. The goal of the algorithm is to improve the motion planning and the object handling capabilities of service robotics platforms that operate in complex and cluttered human environments. Read more