Understanding egocentric human actions with temporal decision forests

Understanding human actions is a fundamental task in computer vision with a wide range of applications including pervasive health-care, robotics and game control. This thesis focuses on the problem of egocentric action recognition from RGB-D data, wherein the world is viewed through the eyes of the actor whose hands describe the actions. The main contributions of this work are its findings regarding egocentric actions as described by hands in two application scenarios and a proposal of a new technique that is based on temporal decision forests. The thesis first introduces a novel framework to recognise fingertip writing in mid-air in the context of human-computer interaction. This framework detects whether the user is writing and tracks the fingertip over time to generate spatio-temporal trajectories that are recognised by using a Hough forest variant that encourages temporal consistency in prediction. A problem with using such forest approach for action recognition is that the learning of temporal dynamics is limited to hand-crafted temporal features and temporal regression, which may break the temporal continuity and lead to inconsistent predictions. To overcome this limitation, the thesis proposes transition forests. Besides any temporal information that is encoded in the feature space, the forest automatically learns the temporal dynamics during training, and it is exploited in inference in an online and efficient manner achieving state-of-the-art results. The last contribution of this thesis is its introduction of the first RGB-D benchmark to allow for the study of egocentric hand-object actions with both hand and object pose annotations. This study conducts an extensive evaluation of different baselines, state-of-the art approaches and temporal decision forest models using colour, depth and hand pose features. Furthermore, it extends the transition forest model to incorporate data from different modalities and demonstrates the benefit of using hand pose features to recognise egocentric human actions. The thesis concludes by discussing and analysing the contributions and proposing a few ideas for future work.Open Acces

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms

Efficient Human Activity Recognition in Large Image and Video Databases

Vision-based human action recognition has attracted considerable interest in recent research for its applications to video surveillance, content-based search, healthcare, and interactive games. Most existing research deals with building informative feature descriptors, designing efficient and robust algorithms, proposing versatile and challenging datasets, and fusing multiple modalities. Often, these approaches build on certain conventions such as the use of motion cues to determine video descriptors, application of off-the-shelf classifiers, and single-factor classification of videos. In this thesis, we deal with important but overlooked issues such as efficiency, simplicity, and scalability of human activity recognition in different application scenarios: controlled video environment (e.g.~indoor surveillance), unconstrained videos (e.g.~YouTube), depth or skeletal data (e.g.~captured by Kinect), and person images (e.g.~Flicker). In particular, we are interested in answering questions like (a) is it possible to efficiently recognize human actions in controlled videos without temporal cues? (b) given that the large-scale unconstrained video data are often of high dimension low sample size (HDLSS) nature, how to efficiently recognize human actions in such data? (c) considering the rich 3D motion information available from depth or motion capture sensors, is it possible to recognize both the actions and the actors using only the motion dynamics of underlying activities? and (d) can motion information from monocular videos be used for automatically determining saliency regions for recognizing actions in still images

Yoga Pose Classification Using Deep Learning

Human pose estimation is a deep-rooted problem in computer vision that has exposed many challenges in the past. Analyzing human activities is beneficial in many fields like video- surveillance, biometrics, assisted living, at-home health monitoring etc. With our fast-paced lives these days, people usually prefer exercising at home but feel the need of an instructor to evaluate their exercise form. As these resources are not always available, human pose recognition can be used to build a self-instruction exercise system that allows people to learn and practice exercises correctly by themselves. This project lays the foundation for building such a system by discussing various machine learning and deep learning approaches to accurately classify yoga poses on prerecorded videos and also in real-time. The project also discusses various pose estimation and keypoint detection methods in detail and explains different deep learning models used for pose classification

Human Pose Estimation from Monocular Images : a Comprehensive Survey

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problema into several modules: feature extraction and description, human body models, and modelin methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used

Visual grasp point localization, classification and state recognition in robotic manipulation of cloth: an overview

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Cloth manipulation by robots is gaining popularity among researchers because of its relevance, mainly (but not only) in domestic and assistive robotics. The required science and technologies begin to be ripe for the challenges posed by the manipulation of soft materials, and many contributions have appeared in the last years. This survey provides a systematic review of existing techniques for the basic perceptual tasks of grasp point localization, state estimation and classification of cloth items, from the perspective of their manipulation by robots. This choice is grounded on the fact that any manipulative action requires to instruct the robot where to grasp, and most garment handling activities depend on the correct recognition of the type to which the particular cloth item belongs and its state. The high inter- and intraclass variability of garments, the continuous nature of the possible deformations of cloth and the evident difficulties in predicting their localization and extension on the garment piece are challenges that have encouraged the researchers to provide a plethora of methods to confront such problems, with some promising results. The present review constitutes for the first time an effort in furnishing a structured framework of these works, with the aim of helping future contributors to gain both insight and perspective on the subjectPeer ReviewedPostprint (author's final draft

Recognition and localization of relevant human behavior in videos, SPIE,

ABSTRACT Ground surveillance is normally performed by human assets, since it requires visual intelligence. However, especially for military operations, this can be dangerous and is very resource intensive. Therefore, unmanned autonomous visualintelligence systems are desired. In this paper, we present an improved system that can recognize actions of a human and interactions between multiple humans. Central to the new system is our agent-based architecture. The system is trained on thousands of videos and evaluated on realistic persistent surveillance data in the DARPA Mind's Eye program, with hours of videos of challenging scenes. The results show that our system is able to track the people, detect and localize events, and discriminate between different behaviors, and it performs 3.4 times better than our previous system

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

On the 3D point cloud for human-pose estimation

This thesis aims at investigating methodologies for estimating a human pose from a 3D point cloud that is captured by a static depth sensor. Human-pose estimation (HPE) is important for a range of applications, such as human-robot interaction, healthcare, surveillance, and so forth. Yet, HPE is challenging because of the uncertainty in sensor measurements and the complexity of human poses. In this research, we focus on addressing challenges related to two crucial components in the estimation process, namely, human-pose feature extraction and human-pose modeling. In feature extraction, the main challenge involves reducing feature ambiguity. We propose a 3D-point-cloud feature called viewpoint and shape feature histogram (VISH) to reduce feature ambiguity by capturing geometric properties of the 3D point cloud of a human. The feature extraction consists of three steps: 3D-point-cloud pre-processing, hierarchical structuring, and feature extraction. In the pre-processing step, 3D points corresponding to a human are extracted and outliers from the environment are removed to retain the 3D points of interest. This step is important because it allows us to reduce the number of 3D points by keeping only those points that correspond to the human body for further processing. In the hierarchical structuring, the pre-processed 3D point cloud is partitioned and replicated into a tree structure as nodes. Viewpoint feature histogram (VFH) and shape features are extracted from each node in the tree to provide a descriptor to represent each node. As the features are obtained based on histograms, coarse-level details are highlighted in large regions and fine-level details are highlighted in small regions. Therefore, the features from the point cloud in the tree can capture coarse level to fine level information to reduce feature ambiguity. In human-pose modeling, the main challenges involve reducing the dimensionality of human-pose space and designing appropriate factors that represent the underlying probability distributions for estimating human poses. To reduce the dimensionality, we propose a non-parametric action-mixture model (AMM). It represents high-dimensional human-pose space using low-dimensional manifolds in searching human poses. In each manifold, a probability distribution is estimated based on feature similarity. The distributions in the manifolds are then redistributed according to the stationary distribution of a Markov chain that models the frequency of human actions. After the redistribution, the manifolds are combined according to a probability distribution determined by action classification. Experiments were conducted using VISH features as input to the AMM. The results showed that the overall error and standard deviation of the AMM were reduced by about 7.9% and 7.1%, respectively, compared with a model without action classification. To design appropriate factors, we consider the AMM as a Bayesian network and propose a mapping that converts the Bayesian network to a neural network called NN-AMM. The proposed mapping consists of two steps: structure identification and parameter learning. In structure identification, we have developed a bottom-up approach to build a neural network while preserving the Bayesian-network structure. In parameter learning, we have created a part-based approach to learn synaptic weights by decomposing a neural network into parts. Based on the concept of distributed representation, the NN-AMM is further modified into a scalable neural network called NND-AMM. A neural-network-based system is then built by using VISH features to represent 3D-point-cloud input and the NND-AMM to estimate 3D human poses. The results showed that the proposed mapping can be utilized to design AMM factors automatically. The NND-AMM can provide more accurate human-pose estimates with fewer hidden neurons than both the AMM and NN-AMM can. Both the NN-AMM and NND-AMM can adapt to different types of input, showing the advantage of using neural networks to design factors