Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

Understanding the Dynamic Visual World: From Motion to Semantics

We live in a dynamic world, which is continuously in motion. Perceiving and interpreting the dynamic surroundings is an essential capability for an intelligent agent. Human beings have the remarkable capability to learn from limited data, with partial or little annotation, in sharp contrast to computational perception models that rely on large-scale, manually labeled data. Reliance on strongly supervised models with manually labeled data inherently prohibits us from modeling the dynamic visual world, as manual annotations are tedious, expensive, and not scalable, especially if we would like to solve multiple scene understanding tasks at the same time. Even worse, in some cases, manual annotations are completely infeasible, such as the motion vector of each pixel (i.e., optical flow) since humans cannot reliably produce these types of labeling. In fact, living in a dynamic world, when we move around, motion information, as a result of moving camera, independently moving objects, and scene geometry, consists of abundant information, revealing the structure and complexity of our dynamic visual world. As the famous psychologist James J. Gibson suggested, “we must perceive in order to move, but we also must move in order to perceive”. In this thesis, we investigate how to use the motion information contained in unlabeled or partially labeled videos to better understand and synthesize the dynamic visual world. This thesis consists of three parts. In the first part, we focus on the “move to perceive” aspect. When moving through the world, it is natural for an intelligent agent to associate image patterns with the magnitude of their displacement over time: as the agent moves, far away mountains don’t move much; nearby trees move a lot. This natural relationship between the appearance of objects and their apparent motion is a rich source of information about the relationship between the distance of objects and their appearance in images. We present a pretext task of estimating the relative depth of elements of a scene (i.e., ordering the pixels in an image according to distance from the viewer) recovered from motion field of unlabeled videos. The goal of this pretext task was to induce useful feature representations in deep Convolutional Neural Networks (CNNs). These induced representations, using 1.1 million video frames crawled from YouTube within one hour without any manual labeling, provide valuable starting features for the training of neural networks for downstream tasks. It is promising to match or even surpass what ImageNet pre-training gives us today, which needs a huge amount of manual labeling, on tasks such as semantic image segmentation as all of our training data comes almost for free. In the second part, we study the “perceive to move” aspect. As we humans look around, we do not solve a single vision task at a time. Instead, we perceive our surroundings in a holistic manner, doing visual understanding using all visual cues jointly. By simultaneously solving multiple tasks together, one task can influence another. In specific, we propose a neural network architecture, called SENSE, which shares common feature representations among four closely-related tasks: optical flow estimation, disparity estimation from stereo, occlusion detection, and semantic segmentation. The key insight is that sharing features makes the network more compact and induces better feature representations. For real-world data, however, not all an- notations of the four tasks mentioned above are always available at the same time. To this end, loss functions are designed to exploit interactions of different tasks and do not need manual annotations, to better handle partially labeled data in a semi- supervised manner, leading to superior understanding performance of the dynamic visual world. Understanding the motion contained in a video enables us to perceive the dynamic visual world in a novel manner. In the third part, we present an approach, called SuperSloMo, which synthesizes slow-motion videos from a standard frame-rate video. Converting a plain video into a slow-motion version enables us to see memorable moments in our life that are hard to see clearly otherwise with naked eyes: a difficult skateboard trick, a dog catching a ball, etc. Such a technique also has wide applications such as generating smooth view transition on a head-mounted virtual reality (VR) devices, compressing videos, synthesizing videos with motion blur, etc

Aprenent a recrear la realitat en 3D

Les tècniques de representació d'escenes en tres dimensions tenen un problema comú : que l'escena es considera com un tot i, per tant, són relativament ineficients a l'hora de realitzar el processament geomètric d'objectes. En aquest treball s'ha proposat una nova tècnica de modelat jeràrquic d'escenes 3D estàtiques que directament té en compte els objectes presents.Investigadores de diversas universidades, entre ellas la UAB, hananalizado la relación entre el conocimiento que tienen de las plantas lasmadres Tsimane' -un grupo étnico de la Amazona boliviana- y la saludde sus hijos. Y han concluido que el estado de salud de los niños demadres que saben más sobre estas plantas es mejor.Researchers of several universities, among them UAB, have analyzedthe relation between the ethnobotanical knowledge of Tsimane ́ mothers-an ethnic group of the Bolivian Amazon- and the health of their children.Researchers have concluded that children with mothers with higherethnobotanical knowledge enjoy better health than the other

Temporally coherent 3D point cloud video segmentation in generic scenes

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Video segmentation is an important building block for high level applications, such as scene understanding and interaction analysis. While outstanding results are achieved in this field by the state-of-the-art learning and model-based methods, they are restricted to certain types of scenes or require a large amount of annotated training data to achieve object segmentation in generic scenes. On the other hand, RGBD data, widely available with the introduction of consumer depth sensors, provide actual world 3D geometry compared with 2D images. The explicit geometry in RGBD data greatly help in computer vision tasks, but the lack of annotations in this type of data may also hinder the extension of learning-based methods to RGBD. In this paper, we present a novel generic segmentation approach for 3D point cloud video (stream data) thoroughly exploiting the explicit geometry in RGBD. Our proposal is only based on low level features, such as connectivity and compactness. We exploit temporal coherence by representing the rough estimation of objects in a single frame with a hierarchical structure and propagating this hierarchy along time. The hierarchical structure provides an efficient way to establish temporal correspondences at different scales of object-connectivity and to temporally manage the splits and merges of objects. This allows updating the segmentation according to the evidence observed in the history. The proposed method is evaluated on several challenging data sets, with promising results for the presented approach.Peer ReviewedPostprint (author's final draft

Trends and concerns in digital cartography

CISRG discussion paper ;

Interactive ray tracing of massive and deformable models

Ray tracing is a fundamental algorithm used for many applications such as computer graphics, geometric simulation, collision detection and line-of-sight computation. Even though the performance of ray tracing algorithms scales with the model complexity, the high memory requirements and the use of static hierarchical structures pose problems with massive models and dynamic data-sets. We present several approaches to address these problems based on new acceleration structures and traversal algorithms. We introduce a compact representation for storing the model and hierarchy while ray tracing triangle meshes that can reduce the memory footprint by up to 80%, while maintaining high performance. As a result, can ray trace massive models with hundreds of millions of triangles on workstations with a few gigabytes of memory. We also show how to use bounding volume hierarchies for ray tracing complex models with interactive performance. In order to handle dynamic scenes, we use refitting algorithms and also present highly-parallel GPU-based algorithms to reconstruct the hierarchies. In practice, our method can construct hierarchies for models with hundreds of thousands of triangles at interactive speeds. Finally, we demonstrate several applications that are enabled by these algorithms. Using deformable BVH and fast data parallel techniques, we introduce a geometric sound propagation algorithm that can run on complex deformable scenes interactively and orders of magnitude faster than comparable previous approaches. In addition, we also use these hierarchical algorithms for fast collision detection between deformable models and GPU rendering of shadows on massive models by employing our compact representations for hybrid ray tracing and rasterization

Representation Learning for Shape Decomposition, By Shape Decomposition

The ability to parse 3D objects into their constituent parts is essential for humans to understand and interact with the surrounding world. Imparting this skill in machines is important for various computer graphics, computer vision, and robotics tasks. Machines endowed with this skill can better interact with its surroundings, perform shape editing, texturing, recomposing, tracking, and animation. In this thesis, we ask two questions. First, how can machines decompose 3D shapes into their fundamental parts? Second, does the ability to decompose the 3D shape into these parts help learn useful 3D shape representations? In this thesis, we focus on parsing the shape into compact representations, such as parametric surface patches and Constructive Solid Geometry (CSG) primitives, which are also widely used representations in 3D modeling in computer graphics. Inspired by the advances in neural networks for 3D shape processing, we develop neural network approaches to tackle shape decomposition. First, we present CSGNet, a network architecture to parse shapes into CSG programs, which is trained using combination of supervised and reinforcement learning. Second, we present ParSeNet, a network architecture to decompose a shape into parametric surface patches (B-Spline) and geometric primitives (plane, cone, cylinder and sphere), trained on a large set of CAD models using supervised learning. The training of deep neural network architectures for 3D recognition and generation tasks requires a large amount of labeled datasets. We explore ways to alleviate this problem by relying on shape decomposition methods to guide the learning process. Towards that end, we first study the use of freely available metadata, albeit inconsistent, from shape repositories to learn 3D shape features. Later we show that learning to decompose a 3D shape into geometric primitives also helps in learning shape representations useful for semantic segmentation tasks. Finally, since most 3D shapes encountered in real life are textured, consisting of several fine-grained semantic parts, we propose a method to learn fine-grained representations for textured 3D shapes in a self-supervised manner by incorporating 3D geometric priors

The robot's vista space : a computational 3D scene analysis

Swadzba A. The robot's vista space : a computational 3D scene analysis. Bielefeld (Germany): Bielefeld University; 2011.The space that can be explored quickly from a fixed view point without locomotion is known as the vista space. In indoor environments single rooms and room parts follow this definition. The vista space plays an important role in situations with agent-agent interaction as it is the directly surrounding environment in which the interaction takes place. A collaborative interaction of the partners in and with the environment requires that both partners know where they are, what spatial structures they are talking about, and what scene elements they are going to manipulate. This thesis focuses on the analysis of a robot's vista space. Mechanisms for extracting relevant spatial information are developed which enable the robot to recognize in which place it is, to detect the scene elements the human partner is talking about, and to segment scene structures the human is changing. These abilities are addressed by the proposed holistic, aligned, and articulated modeling approach. For a smooth human-robot interaction, the computed models should be aligned to the partner's representations. Therefore, the design of the computational models is based on the combination of psychological results from studies on human scene perception with basic physical properties of the perceived scene and the perception itself. The holistic modeling realizes a categorization of room percepts based on the observed 3D spatial layout. Room layouts have room type specific features and fMRI studies have shown that some of the human brain areas being active in scene recognition are sensitive to the 3D geometry of a room. With the aligned modeling, the robot is able to extract the hierarchical scene representation underlying a scene description given by a human tutor. Furthermore, it is able to ground the inferred scene elements in its own visual perception of the scene. This modeling follows the assumption that cognition and language schematize the world in the same way. This is visible in the fact that a scene depiction mainly consists of relations between an object and its supporting structure or between objects located on the same supporting structure. Last, the articulated modeling equips the robot with a methodology for articulated scene part extraction and fast background learning under short and disturbed observation conditions typical for human-robot interaction scenarios. Articulated scene parts are detected model-less by observing scene changes caused by their manipulation. Change detection and background learning are closely coupled because change is defined phenomenologically as variation of structure. This means that change detection involves a comparison of currently visible structures with a representation in memory. In range sensing this comparison can be nicely implement as subtraction of these two representations. The three modeling approaches enable the robot to enrich its visual perceptions of the surrounding environment, the vista space, with semantic information about meaningful spatial structures useful for further interaction with the environment and the human partner