SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving

To mitigate the challenges arising from partial occlusion in human pose keypoint based pedestrian detection methods , we present a novel pedestrian pose keypoint completion method called the separation and dimensionality reduction-based generative adversarial imputation networks (SDR-GAIN) . Firstly, we utilize OpenPose to estimate pedestrian poses in images. Then, we isolate the head and torso keypoints of pedestrians with incomplete keypoints due to occlusion or other factors and perform dimensionality reduction to enhance features and further unify feature distribution. Finally, we introduce two generative models based on the generative adversarial networks (GAN) framework, which incorporate Huber loss, residual structure, and L1 regularization to generate missing parts of the incomplete head and torso pose keypoints of partially occluded pedestrians, resulting in pose completion. Our experiments on MS COCO and JAAD datasets demonstrate that SDR-GAIN outperforms basic GAIN framework, interpolation methods PCHIP and MAkima, machine learning methods k-NN and MissForest in terms of pose completion task. In addition, the runtime of SDR-GAIN is approximately 0.4ms, displaying high real-time performance and significant application value in the field of autonomous driving

A lightweight method for detecting dynamic target occlusions by the robot body

Robot vision is greatly affected by occlusions, which poses challenges to autonomous systems. The robot itself may hide targets of interest from the camera, while it moves within the field of view, leading to failures in task execution. For example, if a target of interest is partially occluded by the robot, detecting and grasping it correctly, becomes very challenging. To solve this problem, we propose a computationally lightweight method to determine the areas that the robot occludes. For this purpose, we use the Unified Robot Description Format (URDF) to generate a virtual depth image of the 3D robot model. Using the virtual depth image, we can effectively determine the partially occluded areas to improve the robustness of the information given by the perception system. Due to the real-time capabilities of the method, it can successfully detect occlusions of moving targets by the moving robot. We validate the effectiveness of the method in an experimental setup using a 6-DoF robot arm and an RGB-D camera by detecting and handling occlusions for two tasks: Pose estimation of a moving object for pickup and human tracking for robot handover. The code is available in \url{https://github.com/auth-arl/virtual\_depth\_image}.Comment: Submitted to RAAD 202

Occlusion-Aware Object Localization, Segmentation and Pose Estimation

We present a learning approach for localization and segmentation of objects in an image in a manner that is robust to partial occlusion. Our algorithm produces a bounding box around the full extent of the object and labels pixels in the interior that belong to the object. Like existing segmentation aware detection approaches, we learn an appearance model of the object and consider regions that do not fit this model as potential occlusions. However, in addition to the established use of pairwise potentials for encouraging local consistency, we use higher order potentials which capture information at the level of im- age segments. We also propose an efficient loss function that targets both localization and segmentation performance. Our algorithm achieves 13.52% segmentation error and 0.81 area under the false-positive per image vs. recall curve on average over the challenging CMU Kitchen Occlusion Dataset. This is a 42.44% decrease in segmentation error and a 16.13% increase in localization performance compared to the state-of-the-art. Finally, we show that the visibility labelling produced by our algorithm can make full 3D pose estimation from a single image robust to occlusion.Comment: British Machine Vision Conference 2015 (poster

3D Hand reconstruction from monocular camera with model-based priors

As virtual and augmented reality (VR/AR) technology gains popularity, facilitating intuitive digital interactions in 3D is of crucial importance. Tools such as VR controllers exist, but such devices support only a limited range of interactions, mapped onto complex sequences of button presses that can be intimidating to learn. In contrast, users already have an instinctive understanding of manual interactions in the real world, which is readily transferable to the virtual world. This makes hands the ideal mode of interaction for down-stream applications such as robotic teleoperation, sign-language translation, and computer-aided design. Existing hand-tracking systems come with several inconvenient limitations. Wearable solutions such as gloves and markers unnaturally limit the range of articulation. Multi-camera systems are not trivial to calibrate and have specialized hardware requirements which make them cumbersome to use. Given these drawbacks, recent research tends to focus on monocular inputs, as these do not constrain articulation and suitable devices are pervasive in everyday life. 3D reconstruction in this setting is severely under-constrained, however, due to occlusions and depth ambiguities. The majority of state-of-the-art works rely on a learning framework to resolve these ambiguities statistically; as a result they have several limitations in common. For example, they require a vast amount of annotated 3D data that is labor intensive to obtain and prone to systematic error. Additionally, traits that are hard to quantify with annotations - the details of individual hand appearance - are difficult to reconstruct in such a framework. Existing methods also make the simplifying assumption that only a single hand is present in the scene. Two-hand interactions introduce additional challenges, however, in the form of inter-hand occlusion, left-right confusion, and collision constraints, that single hand methods cannot address. To tackle the aforementioned shortcomings of previous methods, this thesis advances the state-of-the-art through the novel use of model-based priors to incorporate hand-specific knowledge. In particular, this thesis presents a training method that reduces the amount of annotations required and is robust to systemic biases; it presents the first tracking method that addresses the challenging two-hand-interaction scenario using monocular RGB video, and also the first probabilistic method to model image ambiguity for two-hand interactions. Additionally, this thesis also contributes the first parametric hand texture model with example applications in hand personalization.Virtual- und Augmented-Reality-Technologien (VR/AR) gewinnen rapide an Beliebtheit und Einfluss, und so ist die Erleichterung intuitiver digitaler Interaktionen in 3D von wachsender Bedeutung. Zwar gibt es Tools wie VR-Controller, doch solche Geräte unterstützen nur ein begrenztes Spektrum an Interaktionen, oftmals abgebildet auf komplexe Sequenzen von Tastendrücken, deren Erlernen einschüchternd sein kann. Im Gegensatz dazu haben Nutzer bereits ein instinktives Verständnis für manuelle Interaktionen in der realen Welt, das sich leicht auf die virtuelle Welt übertragen lässt. Dies macht Hände zum idealen Werkzeug der Interaktion für nachgelagerte Anwendungen wie robotergestützte Teleoperation, Übersetzung von Gebärdensprache und computergestütztes Design. Existierende Hand-Tracking Systeme leiden unter mehreren unbequemen Einschränkungen. Tragbare Lösungen wie Handschuhe und aufgesetzte Marker schränken den Bewegungsspielraum auf unnatürliche Weise ein. Systeme mit mehreren Kameras erfordern genaue Kalibrierung und haben spezielle Hardwareanforderungen, die ihre Anwendung umständlich gestalten. Angesichts dieser Nachteile konzentriert sich die neuere Forschung tendenziell auf monokularen Input, da so Bewegungsabläufe nicht gestört werden und geeignete Geräte im Alltag allgegenwärtig sind. Die 3D-Rekonstruktion in diesem Kontext stößt jedoch aufgrund von Okklusionen und Tiefenmehrdeutigkeiten schnell an ihre Grenzen. Die Mehrheit der Arbeiten auf dem neuesten Stand der Technik setzt hierbei auf ein ML-Framework, um diese Mehrdeutigkeiten statistisch aufzulösen; infolgedessen haben all diese mehrere Einschränkungen gemein. Beispielsweise benötigen sie eine große Menge annotierter 3D-Daten, deren Beschaffung arbeitsintensiv und anfällig für systematische Fehler ist. Darüber hinaus sind Merkmale, die mit Anmerkungen nur schwer zu quantifizieren sind – die Details des individuellen Erscheinungsbildes – in einem solchen Rahmen schwer zu rekonstruieren. Bestehende Verfahren gehen auch vereinfachend davon aus, dass nur eine einzige Hand in der Szene vorhanden ist. Zweihand-Interaktionen bringen jedoch zusätzliche Herausforderungen in Form von Okklusion der Hände untereinander, Links-Rechts-Verwirrung und Kollisionsbeschränkungen mit sich, die Einhand-Methoden nicht bewältigen können. Um die oben genannten Mängel früherer Methoden anzugehen, bringt diese Arbeit den Stand der Technik durch die neuartige Verwendung modellbasierter Priors voran, um Hand-spezifisches Wissen zu integrieren. Insbesondere stellt diese Arbeit eine Trainingsmethode vor, die die Menge der erforderlichen Annotationen reduziert und robust gegenüber systemischen Verzerrungen ist; es wird die erste Tracking-Methode vorgestellt, die das herausfordernde Zweihand-Interaktionsszenario mit monokularem RGB-Video angeht, und auch die erste probabilistische Methode zur Modellierung der Bildmehrdeutigkeit für Zweihand-Interaktionen. Darüber hinaus trägt diese Arbeit auch das erste parametrische Handtexturmodell mit Beispielanwendungen in der Hand-Personalisierung bei

MoSculp: Interactive Visualization of Shape and Time

We present a system that allows users to visualize complex human motion via 3D motion sculptures---a representation that conveys the 3D structure swept by a human body as it moves through space. Given an input video, our system computes the motion sculptures and provides a user interface for rendering it in different styles, including the options to insert the sculpture back into the original video, render it in a synthetic scene or physically print it. To provide this end-to-end workflow, we introduce an algorithm that estimates that human's 3D geometry over time from a set of 2D images and develop a 3D-aware image-based rendering approach that embeds the sculpture back into the scene. By automating the process, our system takes motion sculpture creation out of the realm of professional artists, and makes it applicable to a wide range of existing video material. By providing viewers with 3D information, motion sculptures reveal space-time motion information that is difficult to perceive with the naked eye, and allow viewers to interpret how different parts of the object interact over time. We validate the effectiveness of this approach with user studies, finding that our motion sculpture visualizations are significantly more informative about motion than existing stroboscopic and space-time visualization methods.Comment: UIST 2018. Project page: http://mosculp.csail.mit.edu

3D hand pose estimation using convolutional neural networks

3D hand pose estimation plays a fundamental role in natural human computer interactions. The problem is challenging due to complicated variations caused by complex articulations, multiple viewpoints, self-similar parts, severe self-occlusions, different shapes and sizes. To handle these challenges, the thesis makes the following contributions. First, the problem of the multiple viewpoints and complex articulations of hand pose estimation is tackled by decomposing and transforming the input and output space by spatial transformations following the hand structure. By the transformation, both the variation of the input space and output is reduced, which makes the learning easier. The second contribution is a probabilistic framework integrating all the hierarchical regressions. Variants with/without sampling, using different regressors and optimization methods are constructed and compared to provide an insight of the components under this framework. The third contribution is based on the observation that for images with occlusions, there exist multiple plausible configurations for the occluded parts. A hierarchical mixture density network is proposed to handle the multi-modality of the locations for occluded hand joints. It leverages the state-of-the-art hand pose estimators based on Convolutional Neural Networks to facilitate feature learning while models the multiple modes in a two-level hierarchy to reconcile single-valued (for visible joints) and multi-valued (for occluded joints) mapping in its output. In addition, a complete labeled real hand datasets is collected by a tracking system with six 6D magnetic sensors and inverse kinematics to automatically obtain 21-joints hand pose annotations of depth maps.Open Acces