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

Algorithms for Image Analysis in Traffic Surveillance Systems

Import 23/07/2015The presence of various surveillance systems in many areas of the modern society is indisputable and the most perceptible are the video surveillance systems. This thesis mainly describes novel algorithm for vision-based estimation of the parking lot occupancy and the closely related topics of pre-processing of images captured under harsh conditions. The developed algorithms have their practical application in the parking guidance systems which are still more popular. One part of this work also tries to contribute to the specific area of computer graphics denoted as direct volume rendering (DVR).Přítomnost nejrůznějších dohledových systémů v mnoha oblastech soudobé společnosti je nesporná a systémy pro monitorování dopravy jsou těmi nejviditelnějšími. Hlavní část této práce se věnuje popisu nového algoritmu pro detekci obsazenosti parkovacích míst pomocí analýzy obrazu získaného z kamerového systému. Práce se také zabývá tématy úzce souvisejícími s předzpracováním obrazu získaného za ztížených podmínek. Vyvinuté algoritmy mají své praktické uplatnění zejména v oblasti pomocných parkovacích systémů, které se stávají čím dál tím více populárními. Jedna část této práce se snaží přispět do oblasti počítačové grafiky označované jako přímá vizualizace objemových dat.Prezenční460 - Katedra informatikyvyhově

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Richer object representations for object class detection in challenging real world images

Object class detection in real world images has been a synonym for object localization for the longest time. State-of-the-art detection methods, inspired by renowned detection benchmarks, typically target 2D bounding box localization of objects. At the same time, due to the rapid technological and scientific advances, high-level vision applications, aiming at understanding the visual world as a whole, are coming into the focus. The diversity of the visual world challenges these applications in terms of representational complexity, robust inference and training data. As objects play a central role in any vision system, it has been argued that richer object representations, providing higher level of detail than modern detection methods, are a promising direction towards understanding visual scenes. Besides bridging the gap between object class detection and high-level tasks, richer object representations also lead to more natural object descriptions, bringing computer vision closer to human perception. Inspired by these prospects, this thesis explores four different directions towards richer object representations, namely, 3D object representations, fine-grained representations, occlusion representations, as well as understanding convnet representations. Moreover, this thesis illustrates that richer object representations can facilitate high-level applications, providing detailed and natural object descriptions. In addition, the presented representations attain high performance rates, at least on par or often superior to state-of-the-art methods.Detektion von Objektklassen in natürlichen Bildern war lange Zeit gleichbedeutend mit Lokalisierung von Objekten. Von anerkannten Detektions-Benchmarks inspirierte Detektionsmethoden, die auf dem neuesten Stand der Forschung sind, zielen üblicherweise auf die Lokalisierung von Objekten im Bild. Gleichzeitig werden durch den schnellen technologischen und wissenschaftlichen Fortschritt abstraktere Bildverarbeitungsanwendungen, die ein Verständnis der visuellen Welt als Ganzes anstreben, immer interessanter. Die Diversität der visuellen Welt ist eine Herausforderung für diese Anwendungen hinsichtlich der Komplexität der Darstellung, robuster Inferenz und Trainingsdaten. Da Objekte eine zentrale Rolle in jedem Visionssystem spielen, wurde argumentiert, dass reichhaltige Objektrepräsentationen, die höhere Detailgenauigkeit als gegenwärtige Detektionsmethoden bieten, ein vielversprechender Schritt zum Verständnis visueller Szenen sind. Reichhaltige Objektrepräsentationen schlagen eine Brücke zwischen der Detektion von Objektklassen und abstrakteren Aufgabenstellungen, und sie führen auch zu natürlicheren Objektbeschreibungen, wodurch sie die Bildverarbeitung der menschlichen Wahrnehmung weiter annähern. Aufgrund dieser Perspektiven erforscht die vorliegende Arbeit vier verschiedene Herangehensweisen zu reichhaltigeren Objektrepräsentationen

Richer object representations for object class detection in challenging real world images

Object class detection in real world images has been a synonym for object localization for the longest time. State-of-the-art detection methods, inspired by renowned detection benchmarks, typically target 2D bounding box localization of objects. At the same time, due to the rapid technological and scientific advances, high-level vision applications, aiming at understanding the visual world as a whole, are coming into the focus. The diversity of the visual world challenges these applications in terms of representational complexity, robust inference and training data. As objects play a central role in any vision system, it has been argued that richer object representations, providing higher level of detail than modern detection methods, are a promising direction towards understanding visual scenes. Besides bridging the gap between object class detection and high-level tasks, richer object representations also lead to more natural object descriptions, bringing computer vision closer to human perception. Inspired by these prospects, this thesis explores four different directions towards richer object representations, namely, 3D object representations, fine-grained representations, occlusion representations, as well as understanding convnet representations. Moreover, this thesis illustrates that richer object representations can facilitate high-level applications, providing detailed and natural object descriptions. In addition, the presented representations attain high performance rates, at least on par or often superior to state-of-the-art methods.Detektion von Objektklassen in natürlichen Bildern war lange Zeit gleichbedeutend mit Lokalisierung von Objekten. Von anerkannten Detektions-Benchmarks inspirierte Detektionsmethoden, die auf dem neuesten Stand der Forschung sind, zielen üblicherweise auf die Lokalisierung von Objekten im Bild. Gleichzeitig werden durch den schnellen technologischen und wissenschaftlichen Fortschritt abstraktere Bildverarbeitungsanwendungen, die ein Verständnis der visuellen Welt als Ganzes anstreben, immer interessanter. Die Diversität der visuellen Welt ist eine Herausforderung für diese Anwendungen hinsichtlich der Komplexität der Darstellung, robuster Inferenz und Trainingsdaten. Da Objekte eine zentrale Rolle in jedem Visionssystem spielen, wurde argumentiert, dass reichhaltige Objektrepräsentationen, die höhere Detailgenauigkeit als gegenwärtige Detektionsmethoden bieten, ein vielversprechender Schritt zum Verständnis visueller Szenen sind. Reichhaltige Objektrepräsentationen schlagen eine Brücke zwischen der Detektion von Objektklassen und abstrakteren Aufgabenstellungen, und sie führen auch zu natürlicheren Objektbeschreibungen, wodurch sie die Bildverarbeitung der menschlichen Wahrnehmung weiter annähern. Aufgrund dieser Perspektiven erforscht die vorliegende Arbeit vier verschiedene Herangehensweisen zu reichhaltigeren Objektrepräsentationen

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Shape Representations Using Nested Descriptors

The problem of shape representation is a core problem in computer vision. It can be argued that shape representation is the most central representational problem for computer vision, since unlike texture or color, shape alone can be used for perceptual tasks such as image matching, object detection and object categorization. This dissertation introduces a new shape representation called the nested descriptor. A nested descriptor represents shape both globally and locally by pooling salient scaled and oriented complex gradients in a large nested support set. We show that this nesting property introduces a nested correlation structure that enables a new local distance function called the nesting distance, which provides a provably robust similarity function for image matching. Furthermore, the nesting property suggests an elegant flower like normalization strategy called a log-spiral difference. We show that this normalization enables a compact binary representation and is equivalent to a form a bottom up saliency. This suggests that the nested descriptor representational power is due to representing salient edges, which makes a fundamental connection between the saliency and local feature descriptor literature. In this dissertation, we introduce three examples of shape representation using nested descriptors: nested shape descriptors for imagery, nested motion descriptors for video and nested pooling for activities. We show evaluation results for these representations that demonstrate state-of-the-art performance for image matching, wide baseline stereo and activity recognition tasks

Improved robustness and efficiency for automatic visual site monitoring

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 219-228).Knowing who people are, where they are, what they are doing, and how they interact with other people and things is valuable from commercial, security, and space utilization perspectives. Video sensors backed by computer vision algorithms are a natural way to gather this data. Unfortunately, key technical issues persist in extracting features and models that are simultaneously efficient to compute and robust to issues such as adverse lighting conditions, distracting background motions, appearance changes over time, and occlusions. In this thesis, we present a set of techniques and model enhancements to better handle these problems, focusing on contributions in four areas. First, we improve background subtraction so it can better handle temporally irregular dynamic textures. This allows us to achieve a 5.5% drop in false positive rate on the Wallflower waving trees video. Secondly, we adapt the Dalal and Triggs Histogram of Oriented Gradients pedestrian detector to work on large-scale scenes with dense crowds and harsh lighting conditions: challenges which prevent us from easily using a background subtraction solution. These scenes contain hundreds of simultaneously visible people. To make using the algorithm computationally feasible, we have produced a novel implementation that runs on commodity graphics hardware and is up to 76 faster than our CPU-only implementation. We demonstrate the utility of this detector by modeling scene-level activities with a Hierarchical Dirichlet Process.(cont.) Third, we show how one can improve the quality of pedestrian silhouettes for recognizing individual people. We combine general appearance information from a large population of pedestrians with semi-periodic shape information from individual silhouette sequences. Finally, we show how one can combine a variety of detection and tracking techniques to robustly handle a variety of event detection scenarios such as theft and left-luggage detection. We present the only complete set of results on a standardized collection of very challenging videos.by Gerald Edwin Dalley.Ph.D

Multimodal Sensing for Robust and Energy-Efficient Context Detection with Smart Mobile Devices

Adoption of smart mobile devices (smartphones, wearables, etc.) is rapidly growing. There are already over 2 billion smartphone users worldwide [1] and the percentage of smartphone users is expected to be over 50% in the next five years [2]. These devices feature rich sensing capabilities which allow inferences about mobile device user’s surroundings and behavior. Multiple and diverse sensors common on such mobile devices facilitate observing the environment from different perspectives, which helps to increase robustness of inferences and enables more complex context detection tasks. Though a larger number of sensing modalities can be beneficial for more accurate and wider mobile context detection, integrating these sensor streams is non-trivial. This thesis presents how multimodal sensor data can be integrated to facilitate ro- bust and energy efficient mobile context detection, considering three important and challenging detection tasks: indoor localization, indoor-outdoor detection and human activity recognition. This thesis presents three methods for multimodal sensor inte- gration, each applied for a different type of context detection task considered in this thesis. These are gradually decreasing in design complexity, starting with a solution based on an engineering approach decomposing context detection to simpler tasks and integrating these with a particle filter for indoor localization. This is followed by man- ual extraction of features from different sensors and using an adaptive machine learn- ing technique called semi-supervised learning for indoor-outdoor detection. Finally, a method using deep neural networks capable of extracting non-intuitive features di- rectly from raw sensor data is used for human activity recognition; this method also provides higher degree of generalization to other context detection tasks. Energy efficiency is an important consideration in general for battery powered mo- bile devices and context detection is no exception. In the various context detection tasks and solutions presented in this thesis, particular attention is paid to this issue by relying largely on sensors that consume low energy and on lightweight computations. Overall, the solutions presented improve on the state of the art in terms of accuracy and robustness while keeping the energy consumption low, making them practical for use on mobile devices