A Methodology for Extracting Human Bodies from Still Images

Monitoring and surveillance of humans is one of the most prominent applications of today and it is expected to be part of many future aspects of our life, for safety reasons, assisted living and many others. Many efforts have been made towards automatic and robust solutions, but the general problem is very challenging and remains still open. In this PhD dissertation we examine the problem from many perspectives. First, we study the performance of a hardware architecture designed for large-scale surveillance systems. Then, we focus on the general problem of human activity recognition, present an extensive survey of methodologies that deal with this subject and propose a maturity metric to evaluate them. One of the numerous and most popular algorithms for image processing found in the field is image segmentation and we propose a blind metric to evaluate their results regarding the activity at local regions. Finally, we propose a fully automatic system for segmenting and extracting human bodies from challenging single images, which is the main contribution of the dissertation. Our methodology is a novel bottom-up approach relying mostly on anthropometric constraints and is facilitated by our research in the fields of face, skin and hands detection. Experimental results and comparison with state-of-the-art methodologies demonstrate the success of our approach

ImageSpirit: Verbal Guided Image Parsing

Humans describe images in terms of nouns and adjectives while algorithms operate on images represented as sets of pixels. Bridging this gap between how humans would like to access images versus their typical representation is the goal of image parsing, which involves assigning object and attribute labels to pixel. In this paper we propose treating nouns as object labels and adjectives as visual attribute labels. This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images. We propose an efficient (interactive time) solution. Using the extracted labels as handles, our system empowers a user to verbally refine the results. This enables hands-free parsing of an image into pixel-wise object/attribute labels that correspond to human semantics. Verbally selecting objects of interests enables a novel and natural interaction modality that can possibly be used to interact with new generation devices (e.g. smart phones, Google Glass, living room devices). We demonstrate our system on a large number of real-world images with varying complexity. To help understand the tradeoffs compared to traditional mouse based interactions, results are reported for both a large scale quantitative evaluation and a user study.Comment: http://mmcheng.net/imagespirit

Sky-GVINS: a Sky-segmentation Aided GNSS-Visual-Inertial System for Robust Navigation in Urban Canyons

Integrating Global Navigation Satellite Systems (GNSS) in Simultaneous Localization and Mapping (SLAM) systems draws increasing attention to a global and continuous localization solution. Nonetheless, in dense urban environments, GNSS-based SLAM systems will suffer from the Non-Line-Of-Sight (NLOS) measurements, which might lead to a sharp deterioration in localization results. In this paper, we propose to detect the sky area from the up-looking camera to improve GNSS measurement reliability for more accurate position estimation. We present Sky-GVINS: a sky-aware GNSS-Visual-Inertial system based on a recent work called GVINS. Specifically, we adopt a global threshold method to segment the sky regions and non-sky regions in the fish-eye sky-pointing image and then project satellites to the image using the geometric relationship between satellites and the camera. After that, we reject satellites in non-sky regions to eliminate NLOS signals. We investigated various segmentation algorithms for sky detection and found that the Otsu algorithm reported the highest classification rate and computational efficiency, despite the algorithm's simplicity and ease of implementation. To evaluate the effectiveness of Sky-GVINS, we built a ground robot and conducted extensive real-world experiments on campus. Experimental results show that our method improves localization accuracy in both open areas and dense urban environments compared to the baseline method. Finally, we also conduct a detailed analysis and point out possible further directions for future research. For detailed information, visit our project website at https://github.com/SJTU-ViSYS/Sky-GVINS

RGB-D Scene Representations for Prosthetic Vision

This thesis presents a new approach to scene representation for prosthetic vision. Structurally salient information from the scene is conveyed through the prosthetic vision display. Given the low resolution and dynamic range of the display, this enables robust identification and reliable interpretation of key structural features that are missed when using standard appearance-based scene representations. Specifically, two different types of salient structure are investigated: salient edge structure, for depiction of scene shape to the user; and salient object structure, for emulation of biological attention deployment when viewing a scene. This thesis proposes and evaluates novel computer vision algorithms for extracting salient edge and salient object structure from RGB-D input. Extraction of salient edge structure from the scene is first investigated through low-level analysis of surface shape. Our approach is based on the observation that regions of irregular surface shape, such as the boundary between the wall and the floor, tend to be more informative of scene structure than uniformly shaped regions. We detect these surface irregularities through multi-scale analysis of iso-disparity contour orientations, providing a real time method that robustly identifies important scene structure. This approach is then extended by using a deep CNN to learn high level information for distinguishing salient edges from structural texture. A novel depth input encoding called the depth surface descriptor (DSD) is presented, which better captures scene geometry that corresponds to salient edges, improving the learned model. These methods provide robust detection of salient edge structure in the scene. The detection of salient object structure is first achieved by noting that salient objects often have contrasting shape from their surroundings. Contrasting shape in the depth image is captured through the proposed histogram of surface orientations (HOSO) feature. This feature is used to modulate depth and colour contrast in a saliency detection framework, improving the precision of saliency seed regions and through this the accuracy of the final detection. After this, a novel formulation of structural saliency is introduced based on the angular measure of local background enclosure (LBE). This formulation addresses fundamental limitations of depth contrast methods and is not reliant on foreground depth contrast in the scene. Saliency is instead measured through the degree to which a candidate patch exhibits foreground structure. The effectiveness of the proposed approach is evaluated through both standard datasets as well as user studies that measure the contribution of structure-based representations. Our methods are found to more effectively measure salient structure in the scene than existing methods. Our approach results in improved performance compared to standard methods during practical use of an implant display

ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation

We address a weakly-supervised low-shot instance segmentation, an annotation-efficient training method to deal with novel classes effectively. Since it is an under-explored problem, we first investigate the difficulty of the problem and identify the performance bottleneck by conducting systematic analyses of model components and individual sub-tasks with a simple baseline model. Based on the analyses, we propose ENInst with sub-task enhancement methods: instance-wise mask refinement for enhancing pixel localization quality and novel classifier composition for improving classification accuracy. Our proposed method lifts the overall performance by enhancing the performance of each sub-task. We demonstrate that our ENInst is 7.5 times more efficient in achieving comparable performance to the existing fully-supervised few-shot models and even outperforms them at times.Comment: Accepted at Pattern Recognition (PR

Learning to segment in images and videos with different forms of supervision

Much progress has been made in image and video segmentation over the last years. To a large extent, the success can be attributed to the strong appearance models completely learned from data, in particular using deep learning methods. However, to perform best these methods require large representative datasets for training with expensive pixel-level annotations, which in case of videos are prohibitive to obtain. Therefore, there is a need to relax this constraint and to consider alternative forms of supervision, which are easier and cheaper to collect. In this thesis, we aim to develop algorithms for learning to segment in images and videos with different levels of supervision. First, we develop approaches for training convolutional networks with weaker forms of supervision, such as bounding boxes or image labels, for object boundary estimation and semantic/instance labelling tasks. We propose to generate pixel-level approximate groundtruth from these weaker forms of annotations to train a network, which allows to achieve high-quality results comparable to the full supervision quality without any modifications of the network architecture or the training procedure. Second, we address the problem of the excessive computational and memory costs inherent to solving video segmentation via graphs. We propose approaches to improve the runtime and memory efficiency as well as the output segmentation quality by learning from the available training data the best representation of the graph. In particular, we contribute with learning must-link constraints, the topology and edge weights of the graph as well as enhancing the graph nodes - superpixels - themselves. Third, we tackle the task of pixel-level object tracking and address the problem of the limited amount of densely annotated video data for training convolutional networks. We introduce an architecture which allows training with static images only and propose an elaborate data synthesis scheme which creates a large number of training examples close to the target domain from the given first frame mask. With the proposed techniques we show that densely annotated consequent video data is not necessary to achieve high-quality temporally coherent video segmentation results. In summary, this thesis advances the state of the art in weakly supervised image segmentation, graph-based video segmentation and pixel-level object tracking and contributes with the new ways of training convolutional networks with a limited amount of pixel-level annotated training data.In der Bild- und Video-Segmentierung wurden im Laufe der letzten Jahre große Fortschritte erzielt. Dieser Erfolg beruht weitgehend auf starken Appearance Models, die vollständig aus Daten gelernt werden, insbesondere mit Deep Learning Methoden. Für beste Performanz benötigen diese Methoden jedoch große repräsentative Datensätze für das Training mit teuren Annotationen auf Pixelebene, die bei Videos unerschwinglich sind. Deshalb ist es notwendig, diese Einschränkung zu überwinden und alternative Formen des überwachten Lernens in Erwägung zu ziehen, die einfacher und kostengünstiger zu sammeln sind. In dieser Arbeit wollen wir Algorithmen zur Segmentierung von Bildern und Videos mit verschiedenen Ebenen des überwachten Lernens entwickeln. Zunächst entwickeln wir Ansätze zum Training eines faltenden Netzwerkes (convolutional network) mit schwächeren Formen des überwachten Lernens, wie z.B. Begrenzungsrahmen oder Bildlabel, für Objektbegrenzungen und Semantik/Instanz- Klassifikationsaufgaben. Wir schlagen vor, aus diesen schwächeren Formen von Annotationen eine annähernde Ground Truth auf Pixelebene zu generieren, um ein Netzwerk zu trainieren, das hochwertige Ergebnisse ermöglicht, die qualitativ mit denen bei voll überwachtem Lernen vergleichbar sind, und dies ohne Änderung der Netzwerkarchitektur oder des Trainingsprozesses. Zweitens behandeln wir das Problem des beträchtlichen Rechenaufwands und Speicherbedarfs, das der Segmentierung von Videos mittels Graphen eigen ist. Wir schlagen Ansätze vor, um sowohl die Laufzeit und Speichereffizienz als auch die Qualität der Segmentierung zu verbessern, indem aus den verfügbaren Trainingsdaten die beste Darstellung des Graphen gelernt wird. Insbesondere leisten wir einen Beitrag zum Lernen mit must-link Bedingungen, zur Topologie und zu Kantengewichten des Graphen sowie zu verbesserten Superpixeln. Drittens gehen wir die Aufgabe des Objekt-Tracking auf Pixelebene an und befassen uns mit dem Problem der begrenzten Menge von dicht annotierten Videodaten zum Training eines faltenden Netzwerkes. Wir stellen eine Architektur vor, die das Training nur mit statischen Bildern ermöglicht, und schlagen ein aufwendiges Schema zur Datensynthese vor, das aus der gegebenen ersten Rahmenmaske eine große Anzahl von Trainingsbeispielen ähnlich der Zieldomäne schafft. Mit den vorgeschlagenen Techniken zeigen wir, dass dicht annotierte zusammenhängende Videodaten nicht erforderlich sind, um qualitativ hochwertige zeitlich kohärente Resultate der Segmentierung von Videos zu erhalten. Zusammenfassend lässt sich sagen, dass diese Arbeit den Stand der Technik in schwach überwachter Segmentierung von Bildern, graphenbasierter Segmentierung von Videos und Objekt-Tracking auf Pixelebene weiter entwickelt, und mit neuen Formen des Trainings faltender Netzwerke bei einer begrenzten Menge von annotierten Trainingsdaten auf Pixelebene einen Beitrag leistet