Information acquisition using eye-gaze tracking for person-following with mobile robots

In the effort of developing natural means for human-robot interaction (HRI), signifcant amount of research has been focusing on Person-Following (PF) for mobile robots. PF, which generally consists of detecting, recognizing and following people, is believed to be one of the required functionalities for most future robots that share their environments with their human companions. Research in this field is mostly directed towards fully automating this functionality, which makes the challenge even more tedious. Focusing on this challenge leads research to divert from other challenges that coexist in any PF system. A natural PF functionality consists of a number of tasks that are required to be implemented in the system. However, in more realistic life scenarios, not all the tasks required for PF need to be automated. Instead, some of these tasks can be operated by human operators and therefore require natural means of interaction and information acquisition. In order to highlight all the tasks that are believed to exist in any PF system, this paper introduces a novel taxonomy for PF. Also, in order to provide a natural means for HRI, TeleGaze is used for information acquisition in the implementation of the taxonomy. TeleGaze was previously developed by the authors as a means of natural HRI for teleoperation through eye-gaze tracking. Using TeleGaze in the aid of developing PF systems is believed to show the feasibility of achieving a realistic information acquisition in a natural way

Multi-Sensor Person Following in Low-Visibility Scenarios

Person following with mobile robots has traditionally been an important research topic. It has been solved, in most cases, by the use of machine vision or laser rangefinders. In some special circumstances, such as a smoky environment, the use of optical sensors is not a good solution. This paper proposes and compares alternative sensors and methods to perform a person following in low visibility conditions, such as smoky environments in firefighting scenarios. The use of laser rangefinder and sonar sensors is proposed in combination with a vision system that can determine the amount of smoke in the environment. The smoke detection algorithm provides the robot with the ability to use a different combination of sensors to perform robot navigation and person following depending on the visibility in the environment

A novel visual tracking scheme for unstructured indoor environments

In the ever-expanding sphere of assistive robotics, the pressing need for advanced methods capable of accurately tracking individuals within unstructured indoor settings has been magnified. This research endeavours to devise a realtime visual tracking mechanism that encapsulates high performance attributes while maintaining minimal computational requirements. Inspired by the neural processes of the human brain’s visual information handling, our innovative algorithm employs a pattern image, serving as an ephemeral memory, which facilitates the identification of motion within images. This tracking paradigm was subjected to rigorous testing on a Nao humanoid robot, demonstrating noteworthy outcomes in controlled laboratory conditions. The algorithm exhibited a remarkably low false detection rate, less than 4%, and target losses were recorded in merely 12% of instances, thus attesting to its successful operation. Moreover, the algorithm’s capacity to accurately estimate the direct distance to the target further substantiated its high efficacy. These compelling findings serve as a substantial contribution to assistive robotics. The proficient visual tracking methodology proposed herein holds the potential to markedly amplify the competencies of robots operating in dynamic, unstructured indoor settings, and set the foundation for a higher degree of complex interactive tasks

Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes

In this paper we address the problem of multiple camera calibration in the presence of a homogeneous scene, and without the possibility of employing calibration object based methods. The proposed solution exploits salient features present in a larger field of view, but instead of employing active vision we replace the cameras with stereo rigs featuring a long focal analysis camera, as well as a short focal registration camera. Thus, we are able to propose an accurate solution which does not require intrinsic variation models as in the case of zooming cameras. Moreover, the availability of the two views simultaneously in each rig allows for pose re-estimation between rigs as often as necessary. The algorithm has been successfully validated in an indoor setting, as well as on a difficult scene featuring a highly dense pilgrim crowd in Makkah.Comment: 13 pages, 6 figures, submitted to Machine Vision and Application

CompaRob: the shopping cart assistance robot

Technology has recently been developed which offers an excellent opportunity to design systems with the ability to help people in their own houses. In particular, assisting elderly people in their environments is something that can significantly improve their quality of life. However, helping elderly people outside their usual environment is also necessary, to help them to carry out daily tasks like shopping. In this paper we present a person-following shopping cart assistance robot, capable of helping elderly people to carry products in a supermarket. First of all, the paper presents a survey of related systems that perform this task, using different approaches, such as attachable modules and computer vision. After that, the paper describes in detail the proposed system and its main features. The cart uses ultrasonic sensors and radio signals to provide a simple and effective person localization and following method. Moreover, the cart can be connected to a portable device like a smartphone or tablet, thus providing ease of use to the end user. The prototype has been tested in a grocery store, while simulations have been done to analyse its scalability in larger spaces where multiple robots could coexist.This work was partly supported by Spanish Ministry under Grant DPI2014-57746-C3 (MERBOTS Project) and by Universitat Jaume I Grants P1-1B2015-68 and PID2010-12

Person Detection and Tracking Using Binocular Lucas-Kanade Feature Tracking and K-means Clustering

In this thesis, we present the design and implementation of a method for real-time person detection and tracking. Many current methods for detecting and tracking people rely on color contrast or movement to segment the image. Using color, however, requires the target and the background to be significantly different, and motion segmentation requires the target to be in constant motion relative to the background, often requiring stationary cameras. Pattern detection methods have also been applied to the problem of detecting pedestrians, but these approaches are slower and require stationary cameras to function. The method we present in this work does not require a color difference or constant motion to operate. We use Lucas-Kanade features to track feature points between left and right images, producing a sparse disparity map which is then segmented through the application of k-means clustering. We apply a Viola-Jones face detector to determine which, if any, of the resulting feature clusters represent a trackable person. This algorithm is tested using two identical standard cameras mounted on a mobile robot platform. Results are presented demonstrating detection and tracking of a person in several different situations, including partial occlusion and self-occlusion

Unsupervised Camera Localization in Crowded Spaces

Existing camera networks in public spaces such as train terminals or malls can help social robots to navigate crowded scenes. However, the localization of the cameras is required, i.e., the positions and poses of all cameras in a unique reference. In this work, we estimate the relative location of any pair of cameras by solely using noisy trajectories observed from each camera. We propose a fully unsupervised learningtechniqueusingunlabelledpedestriansmotionpatterns captured in crowded scenes. We first estimate the pairwise camera parameters by optimally matching single-view pedestrian tracks using social awareness. Then, we show the impact of jointly estimating the network parameters. This is done by formulating a nonlinear least square optimization problem, leveraging a continuous approximation of the matching function. We evaluate our approach in real-world environments such as train terminals, whereseveralhundredsofindividualsneedtobetrackedacross dozens of cameras every second