59,294 research outputs found
Path planning for socially-aware humanoid robots
Designing efficient autonomous navigation systems for mobile robots involves consideration of the robotĂs environment while arriving at a systems architecture that trades off multiple constraints. We have architected a navigation framework for socially-aware autonomous robot navigation, using only the on-board computing resources. Our goal is to foster the development of several important service robotics applications using this platform. Our framework allows a robot to autonomously navigate in indoor environments while accounting for people (i.e., estimating the path of all individuals in the environment), respecting each individualĂs private space.
In our design, we can leverage a wide number of sensors for navigation, including cameras, 2D and 3D scanners, and motion trackers. When designing our sensor system, we have considered that mobile robots have limited resources (i.e., power and computation) and that some sensors are costlier than others (e.g., cameras and 3D scanners stream data at high rates), requiring intensive computation to provide useful insight for real-time navigation. We tradeoff between accuracy, responsiveness, and power, and choose a Hokuyo UST-20LX 2D laser scanner for robot localization, obstacle detection and people tracking. We use an MPU-6050 for motion tracking.
Our navigation framework features a low-power sensor system (< 5W) tailored for improved battery life in robotic applications while providing sufficient accuracy. We have completed a prototype for a Human Support Robot using the available onboard computing devices, requiring less than 60W to run. We estimate we can obtain similar performance, while reducing power by ~60%, utilizing low-power high-performance accelerator hardware and parallelized software.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tec
Tracking human motion with multiple cameras using articulated ICP with hard constraints
Questa tesi propone un nuovo algoritmo basato su ICP per il tracking di un modello
scheletrico articolato di un corpo umano. L\u2019algoritmo proposto prende in input immagini
calibrate di un soggetto, calcola la ricostruzione volumetrica e la linea mediale del corpo
e quindi posiziona in modo adeguato il modello, composto di segmenti, in ogni frame
usando una versione di ICP modificata (versione che usa una strategia di attraversamento
alberi gerarchica che mantiene connessi tutti i segmenti del modello nei giunti relativi).
L\u2019approccio proposto usa limiti cinematica per i giunti e un filtro di Kalman esteso per
fare il tracking del modello.
Il primo contributo originale di questa tesi \ue8 l\u2019algoritmo per trovare i punti sullo scheletro
di un volume tridimensionale. L\u2019algoritmo, usando una tecnica di slicing trova l\u2019asse
mediale di un volume 3D in modo veloce utilizzando il processore della scheda grafica e
le texture units della scheda stessa. Questo algoritmo produce ottimi risultati per quanto
riguarda la qualit\ue0 e le prestazioni se comparato con altri algoritmi in letteratura.
Un altro contributo originale \ue8 l\u2019introduzione di una nuova strategia di tracking basata su
un approccio gerarchico dell\u2019algoritmo ICP, utilizzato per trovare le congruenze tra un
modello di corpo umano composto da soli segmenti e un insieme di punti 3D.
L\u2019algoritmo usa una versione di ICP dove tutti i punti 3D sono pesati in funzione del
segmento del corpo preso in considerazione dall\u2019algoritmo in quel momento.
L\u2019applicazione di queste tecniche dimostra la bont\ue0 del metodo e le prestazioni ottenute
in termini di qualit\ue0 della stima della posa sono comparabili con altri lavori in letteratura.
I risultati presentati nella tesi dimostrano la fattibilit\ue0 dell\u2019approccio generale, che si
intende utilizzare in un sistema completo per il tracking di corpi umani senza l\u2019uso di
marcatori. In futuro il lavoro pu\uf2 essere esteso ottimizzando l\u2019implementazione e la
codifica in modo da poter ottenere prestazioni real-time.This thesis proposed a new ICP-based algorithm for tracking articulated skeletal model of
a human body. The proposed algorithm takes as input multiple calibrated views of the
subject, computes a volumetric reconstruction and the centerlines of the body and fits the
skeletal body model in each frame using a hierarchic tree traversal version of the ICP
algorithm that preserves the connection of the segments at the joints. The proposed
approach uses the kinematic constraints and an Extended Kalman Filter to track the body
pose.
The first contribution is a new algorithm to find the skeletal points of a 3D volume. The
algorithm using a slicing technique find the medial axis of a volume in a fast way using
the graphic card processor and the texture units. This algorithm produce good results in
quality and performance compared to other works in literature.
Another contribution is the introduction of a new tracking strategy based on a
hierarchical application of the ICP standard algorithm to find the match between a stick
body model and a set of 3D points. The algorithm use a traversing version of ICP where
also all the 3D points are weighted in such a way every limbs of the model can best fit on
the right portion of the body.
The application of these techniques shown the feasibility of the method and the
performances obtained in terms of quality of estimate pose are comparable
with other works in literature.
The results presented here demonstrate the feasibility of the approach, which is is
intended to be used in complete system for vision-based markerless human body
tracking. Future work will aimed at optimizing the implementation, in order to achieve
real-time performances
Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras
Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.<br/
Multiple detections application for indoor tracking using PIR sensor and Kalman filter
Recently, human tracking in multiple indoor environments is getting broadly in demand to enhance security surveillance. Traditional passive video surveillance shown that it has working ineffectively nowadays because the number of cameras has exceeded the ability of operators to monitor them. In this paper, we proposed methods of detecting human presence using Pyroelectric Infrared (PIR) Motion Sensor and tracking people in multiple indoor locations using Kalman filter-based estimation. The proposed method is implemented to analyze the movement of people within the prescribed area and the result will be presented in footprint mapping of the said area. This will further enhanced building security surveillance especially at the sensitive or restricted areas. Experiments for single target tracking in several areas are carried out to verify the application of the developed system. As the results, the maximum error for tracking trajectory reduced from 0.28m to 0.19m and average error for tracking trajectory also reduced from 0.10m to 0.07m after using Kalman filter estimation algorithm
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart
Glasses, Computer Vision, Video Analytics, Human-machine Interactio
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