3,442 research outputs found
Data association and occlusion handling for vision-based people tracking by mobile robots
This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets
Survey on Vision-based Path Prediction
Path prediction is a fundamental task for estimating how pedestrians or
vehicles are going to move in a scene. Because path prediction as a task of
computer vision uses video as input, various information used for prediction,
such as the environment surrounding the target and the internal state of the
target, need to be estimated from the video in addition to predicting paths.
Many prediction approaches that include understanding the environment and the
internal state have been proposed. In this survey, we systematically summarize
methods of path prediction that take video as input and and extract features
from the video. Moreover, we introduce datasets used to evaluate path
prediction methods quantitatively.Comment: DAPI 201
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives
The importance of depth perception in the interactions that humans have
within their nearby space is a well established fact. Consequently, it is also
well known that the possibility of exploiting good stereo information would
ease and, in many cases, enable, a large variety of attentional and interactive
behaviors on humanoid robotic platforms. However, the difficulty of computing
real-time and robust binocular disparity maps from moving stereo cameras often
prevents from relying on this kind of cue to visually guide robots' attention
and actions in real-world scenarios. The contribution of this paper is
two-fold: first, we show that the Efficient Large-scale Stereo Matching
algorithm (ELAS) by A. Geiger et al. 2010 for computation of the disparity map
is well suited to be used on a humanoid robotic platform as the iCub robot;
second, we show how, provided with a fast and reliable stereo system,
implementing relatively challenging visual behaviors in natural settings can
require much less effort. As a case of study we consider the common situation
where the robot is asked to focus the attention on one object close in the
scene, showing how a simple but effective disparity-based segmentation solves
the problem in this case. Indeed this example paves the way to a variety of
other similar applications
Stereo Vision Tracking of Multiple Objects in Complex Indoor Environments
This paper presents a novel system capable of solving the problem of tracking multiple targets in a crowded, complex and dynamic indoor environment, like those typical of mobile robot applications. The proposed solution is based on a stereo vision set in the acquisition step and a probabilistic algorithm in the obstacles position estimation process. The system obtains 3D position and speed information related to each object in the robotâs environment; then it achieves a classification between building elements (ceiling, walls, columns and so on) and the rest of items in robot surroundings. All objects in robot surroundings, both dynamic and static, are considered to be obstacles but the structure of the environment itself. A combination of a Bayesian algorithm and a deterministic clustering process is used in order to obtain a multimodal representation of speed and position of detected obstacles. Performance of the final system has been tested against state of the art proposals; test results validate the authorsâ proposal. The designed algorithms and procedures provide a solution to those applications where similar multimodal data structures are found
Spatial context-aware person-following for a domestic robot
Domestic robots are in the focus of research in
terms of service providers in households and even as robotic
companion that share the living space with humans. A major
capability of mobile domestic robots that is joint exploration
of space. One challenge to deal with this task is how could we
let the robots move in space in reasonable, socially acceptable
ways so that it will support interaction and communication
as a part of the joint exploration. As a step towards this
challenge, we have developed a context-aware following behav-
ior considering these social aspects and applied these together
with a multi-modal person-tracking method to switch between
three basic following approaches, namely direction-following,
path-following and parallel-following. These are derived from
the observation of human-human following schemes and are
activated depending on the current spatial context (e.g. free
space) and the relative position of the interacting human.
A combination of the elementary behaviors is performed in
real time with our mobile robot in different environments.
First experimental results are provided to demonstrate the
practicability of the proposed approach
Multisensor data fusion for joint people tracking and identification with a service robot
Tracking and recognizing people are essential skills modern service robots have to be provided with. The two tasks are generally performed independently, using ad-hoc solutions that first estimate the location of humans and then proceed with their identification. The solution presented in this paper, instead, is a general framework for tracking and recognizing people simultaneously with a mobile robot, where the estimates of the human location and identity are fused using probabilistic techniques. Our approach takes inspiration from recent implementations of joint tracking and classification, where the considered targets are mainly vehicles and aircrafts in military and civilian applications. We illustrate how people can be robustly tracked and recognized with a service robot using an improved histogram-based detection and multisensor data fusion. Some experiments in real challenging scenarios show the good performance of our solution
Continuous Human Activity Tracking over a Large Area with Multiple Kinect Sensors
In recent years, researchers had been inquisitive about the use of technology to enhance the healthcare and wellness of patients with dementia. Dementia symptoms are associated with the decline in thinking skills and memory severe enough to reduce a personâs ability to pay attention and perform daily activities. Progression of dementia can be assessed by monitoring the daily activities of the patients. This thesis encompasses continuous localization and behavioral analysis of patientâs motion pattern over a wide area indoor living space using multiple calibrated Kinect sensors connected over the network. The skeleton data from all the sensor is transferred to the host computer via TCP sockets into Unity software where it is integrated into a single world coordinate system using calibration technique. Multiple cameras are placed with some overlap in the field of view for the successful calibration of the cameras and continuous tracking of the patients. Localization and behavioral data are stored in a CSV file for further analysis
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