496 research outputs found
Human-robot interaction and computer-vision-based services for autonomous robots
L'Aprenentatge per Imitació (IL), o Programació de robots per Demostració (PbD), abasta mètodes pels quals un robot aprèn noves habilitats a través de l'orientació humana i la imitació. La PbD s'inspira en la forma en què els éssers humans aprenen noves habilitats per imitació amb la finalitat de desenvolupar mètodes pels quals les noves tasques es poden transferir als robots. Aquesta tesi està motivada per la pregunta genèrica de "què imitar?", Que es refereix al problema de com extreure les característiques essencials d'una tasca. Amb aquesta finalitat, aquí adoptem la perspectiva del Reconeixement d'Accions (AR) per tal de permetre que el robot decideixi el què cal imitar o inferir en interactuar amb un ésser humà.
L'enfoc proposat es basa en un mètode ben conegut que prové del processament del llenguatge natural: és a dir, la bossa de paraules (BoW). Aquest mètode s'aplica a grans bases de dades per tal d'obtenir un model entrenat. Encara que BoW és una tècnica d'aprenentatge de màquines que s'utilitza en diversos camps de la investigació, en la classificació d'accions per a l'aprenentatge en robots està lluny de ser acurada. D'altra banda, se centra en la classificació d'objectes i gestos en lloc d'accions. Per tant, en aquesta tesi es demostra que el mètode és adequat, en escenaris de classificació d'accions, per a la fusió d'informació de diferents fonts o de diferents assajos.
Aquesta tesi fa tres contribucions: (1) es proposa un mètode general per fer front al reconeixement d'accions i per tant contribuir a l'aprenentatge per imitació; (2) la metodologia pot aplicar-se a grans bases de dades, que inclouen diferents modes de captura de les accions; i (3) el mètode s'aplica específicament en un projecte internacional d'innovació real anomenat Vinbot.El Aprendizaje por Imitación (IL), o Programación de robots por Demostración (PbD), abarca métodos por los cuales un robot aprende nuevas habilidades a través de la orientación humana y la imitación. La PbD se inspira en la forma en que los seres humanos aprenden nuevas habilidades por imitación con el fin de desarrollar métodos por los cuales las nuevas tareas se pueden transferir a los robots. Esta tesis está motivada por la pregunta genérica de "qué imitar?", que se refiere al problema de cómo extraer las características esenciales de una tarea. Con este fin, aquí adoptamos la perspectiva del Reconocimiento de Acciones (AR) con el fin de permitir que el robot decida lo que hay que imitar o inferir al interactuar con un ser humano.
El enfoque propuesto se basa en un método bien conocido que proviene del procesamiento del lenguaje natural: es decir, la bolsa de palabras (BoW). Este método se aplica a grandes bases de datos con el fin de obtener un modelo entrenado. Aunque BoW es una técnica de aprendizaje de máquinas que se utiliza en diversos campos de la investigación, en la clasificación de acciones para el aprendizaje en robots está lejos de ser acurada. Además, se centra en la clasificación de objetos y gestos en lugar de acciones. Por lo tanto, en esta tesis se demuestra que el método es adecuado, en escenarios de clasificación de acciones, para la fusión de información de diferentes fuentes o de diferentes ensayos.
Esta tesis hace tres contribuciones: (1) se propone un método general para hacer frente al reconocimiento de acciones y por lo tanto contribuir al aprendizaje por imitación; (2) la metodología puede aplicarse a grandes bases de datos, que incluyen diferentes modos de captura de las acciones; y (3) el método se aplica específicamente en un proyecto internacional de innovación real llamado Vinbot.Imitation Learning (IL), or robot Programming by Demonstration (PbD), covers methods by which a robot learns new skills through human guidance and imitation. PbD takes its inspiration from the way humans learn new skills by imitation in order to develop methods by which new tasks can be transmitted to robots. This thesis is motivated by the generic question of “what to imitate?” which concerns the problem of how to extract the essential features of a task. To this end, here we adopt Action Recognition (AR) perspective in order to allow the robot to decide what has to be imitated or inferred when interacting with a human kind.
The proposed approach is based on a well-known method from natural language processing: namely, Bag of Words (BoW). This method is applied to large databases in order to obtain a trained model. Although BoW is a machine learning technique that is used in various fields of research, in action classification for robot learning it is far from accurate. Moreover, it focuses on the classification of objects and gestures rather than actions. Thus, in this thesis we show that the method is suitable in action classification scenarios for merging information from different sources or different trials.
This thesis makes three contributions: (1) it proposes a general method for dealing with action recognition and thus to contribute to imitation learning; (2) the methodology can be applied to large databases which include different modes of action captures; and (3) the method is applied specifically in a real international innovation project called Vinbot
Efficient Body Motion Quantification and Similarity Evaluation Using 3-D Joints Skeleton Coordinates
Feature Extraction and Recognition for Human Action Recognition
How to automatically label videos containing human motions is the task of human action recognition. Traditional human action recognition algorithms use the RGB videos as input, and it is a challenging task because of the large intra-class variations of actions, cluttered background, possible camera movement, and illumination variations. Recently, the introduction of cost-effective depth cameras provides a new possibility to address difficult issues. However, it also brings new challenges such as noisy depth maps and time alignment. In this dissertation, effective and computationally efficient feature extraction and recognition algorithms are proposed for human action recognition.
At the feature extraction step, two novel spatial-temporal feature descriptors are proposed which can be combined with local feature detectors. The first proposed descriptor is the Shape and Motion Local Ternary Pattern (SMltp) descriptor which can dramatically reduced the number of features generated by dense sampling without sacrificing the accuracy. In addition, the Center-Symmetric Motion Local Ternary Pattern (CS-Mltp) descriptor is proposed, which describes the spatial and temporal gradients-like features. Both descriptors (SMltp and CS-Mltp) take advantage of the Local Binary Pattern (LBP) texture operator in terms of tolerance to illumination change, robustness in homogeneous region and computational efficiency.
For better feature representation, this dissertation presents a new Dictionary Learning (DL) method to learn an overcomplete set of representative vectors (atoms) so that any input feature can be approximated by a linear combination of these atoms with minimum reconstruction error. Instead of simultaneously learning one overcomplete dictionary for all classes, we learn class-specific sub-dictionaries to increase the discrimination. In addition, the group sparsity and the geometry constraint are added to the learning process to further increase the discriminative power, so that features are well reconstructed by atoms from the same class and features from the same class with high similarity will be forced to have similar coefficients.
To evaluate the proposed algorithms, three applications including single view action recognition, distributed multi-view action recognition, and RGB-D action recognition have been explored. Experimental results on benchmark datasets and comparative analyses with the state-of-the-art methods show the effectiveness and merits of the proposed algorithms
DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-Reflectors
In this paper, a marker-based, single-person optical motion capture method (DeepMoCap) is proposed using multiple spatio-temporally aligned infrared-depth sensors and retro-reflective straps and patches (reflectors). DeepMoCap explores motion capture by automatically localizing and labeling reflectors on depth images and, subsequently, on 3D space. Introducing a non-parametric representation to encode the temporal correlation among pairs of colorized depthmaps and 3D optical flow frames, a multi-stage Fully Convolutional Network (FCN) architecture is proposed to jointly learn reflector locations and their temporal dependency among sequential frames. The extracted reflector 2D locations are spatially mapped in 3D space, resulting in robust 3D optical data extraction. The subject’s motion is efficiently captured by applying a template-based fitting technique on the extracted optical data. Two datasets have been created and made publicly available for evaluation purposes; one comprising multi-view depth and 3D optical flow annotated images (DMC2.5D), and a second, consisting of spatio-temporally aligned multi-view depth images along with skeleton, inertial and ground truth MoCap data (DMC3D). The FCN model outperforms its competitors on the DMC2.5D dataset using 2D Percentage of Correct Keypoints (PCK) metric, while the motion capture outcome is evaluated against RGB-D and inertial data fusion approaches on DMC3D, outperforming the next best method by 4.5% in total 3D PCK accuracy
Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception
We introduce the Aria Digital Twin (ADT) - an egocentric dataset captured
using Aria glasses with extensive object, environment, and human level ground
truth. This ADT release contains 200 sequences of real-world activities
conducted by Aria wearers in two real indoor scenes with 398 object instances
(324 stationary and 74 dynamic). Each sequence consists of: a) raw data of two
monochrome camera streams, one RGB camera stream, two IMU streams; b) complete
sensor calibration; c) ground truth data including continuous
6-degree-of-freedom (6DoF) poses of the Aria devices, object 6DoF poses, 3D eye
gaze vectors, 3D human poses, 2D image segmentations, image depth maps; and d)
photo-realistic synthetic renderings. To the best of our knowledge, there is no
existing egocentric dataset with a level of accuracy, photo-realism and
comprehensiveness comparable to ADT. By contributing ADT to the research
community, our mission is to set a new standard for evaluation in the
egocentric machine perception domain, which includes very challenging research
problems such as 3D object detection and tracking, scene reconstruction and
understanding, sim-to-real learning, human pose prediction - while also
inspiring new machine perception tasks for augmented reality (AR) applications.
To kick start exploration of the ADT research use cases, we evaluated several
existing state-of-the-art methods for object detection, segmentation and image
translation tasks that demonstrate the usefulness of ADT as a benchmarking
dataset
Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network
Drone systems have been deployed by various law enforcement agencies to
monitor hostiles, spy on foreign drug cartels, conduct border control
operations, etc. This paper introduces a real-time drone surveillance system to
identify violent individuals in public areas. The system first uses the Feature
Pyramid Network to detect humans from aerial images. The image region with the
human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network
for human pose estimation. The orientations between the limbs of the estimated
pose are next used to identify the violent individuals. The proposed deep
network can learn meaningful representations quickly using ScatterNet and
structural priors with relatively fewer labeled examples. The system detects
the violent individuals in real-time by processing the drone images in the
cloud. This research also introduces the aerial violent individual dataset used
for training the deep network which hopefully may encourage researchers
interested in using deep learning for aerial surveillance. The pose estimation
and violent individuals identification performance is compared with the
state-of-the-art techniques.Comment: To Appear in the Efficient Deep Learning for Computer Vision (ECV)
workshop at IEEE Computer Vision and Pattern Recognition (CVPR) 2018. Youtube
demo at this: https://www.youtube.com/watch?v=zYypJPJipY
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