Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

This paper presents and characterizes the Princeton Application Repository for Shared-Memory Computers (PARSEC), a benchmark suite for studies of Chip-Multiprocessors (CMPs). Previous available benchmarks for multiprocessors have focused on high-performance computing applications and used a limited number of synchronization methods. PARSEC includes emerging applications in recognition, mining and synthesis (RMS) as well as systems applications which mimic large-scale multithreaded commercial programs. Our characterization shows that the benchmark suite covers a wide spectrum of working sets, locality, data sharing, synchronization and off-chip traffic. The benchmark suite has been made available to the public.

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The PARSEC benchmark suite: characterization and architectural implications

http://cvssp-data.eps.surrey.ac.uk/Personal/AdrianHilton/Publications_files/survey_techrep.pdf

A survey of advances in vision-based human motion capture and analysis

We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms. Besides increasing the size of the datasets in the current state-of-the-art by several orders of magnitude, we also aim to complement such datasets with a diverse set of motions and poses encountered as part of typical human activities (taking photos, talking on the phone, posing, greeting, eating, etc.), with additional synchronized image, human motion capture, and time of flight (depth) data, and with accurate 3D body scans of all the subject actors involved. We also provide controlled mixed reality evaluation scenarios where 3D human models are animated using motion capture and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide a set of large-scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. Our experiments show that our best large-scale model can leverage our full training set to obtain a 20% improvement in performance compared to a training set of the scale of the largest existing public dataset for this problem. Yet the potential for improvement by leveraging higher capacity, more complex models with our large dataset, is substantially vaster and should stimulate future research. The dataset together with code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, is available online at http://vision.imar.ro/human3.6m .

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Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments

Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance, and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspectives. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection system and the underlying models. The second (and larger) part of the paper contains a corresponding experimental study. We consider a diverse set of state-of-the-art systems: wavelet-based AdaBoost cascade, HOG/linSVM, NN/LRF, and combined shape-texture detection. Experiments are performed on an extensive data set captured onboard a vehicle driving through urban environment. The data set includes many thousands of training samples as well as a 27-minute test sequence involving more than 20,000 images with annotated pedestrian locations. We consider a generic evaluation setting and one specific to pedestrian detection onboard a vehicle. Results indicate a clear advantage of HOG/linSVM at higher image resolutions and lower processing speeds, and a superiority of the wavelet-based AdaBoost cascade approach at lower image resolutions and (near) real-time processing speeds. The data set (8.5 GB) is made public for benchmarking purposes.

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Monocular Pedestrian Detection: Survey and Experiments

The main challenge in articulated body motion tracking is the large number of degrees of freedom (around 30) to be recovered. Search algorithms, either deterministic or stochastic, that search such a space without constraint, fall foul of exponential computational complexity. One approach is to introduce constraints: either labelling using markers or colour coding, prior assumptions about motion trajectories or view restrictions. Another is to relax constraints arising from articulation, and track limbs as if their motions were independent. In contrast, we aim for general tracking without special preparation of objects or restrictive assumptions. The principal contribution of the paper is the development of a modified particle filter for search in high dimensional configuration spaces. It uses a continuation principle based on annealing to introduce the influence of narrow peaks in the fitness function, gradually. The new algorithm, termed annealed particle filtering, is shown to be capable of recovering full articulated body motion efficiently.

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Articulated body motion capture by annealed particle filtering

We develop a modified particle filter which is shown to be effective at searching the high-dimensional configuration spaces (c. 30 + dimensions) encountered in visual tracking of articulated body motion. The algorithm uses a continuation principle, based on annealing, to introduce the influence of narrow peaks in the fitness function, gradually. The new algorithm, termed annealed particle filtering, is shown to be capable of recovering full articulated body motion efficiently. A mechanism for achieving a soft partitioning of the search space is described and implemented, and shown to improve the algorithm's performance. Likewise, the introduction of a crossover operator is shown to improve the effectiveness of the search for kinematic trees (such as a human body). Results are given for a variety of agile motions such as walking, running and jumping.

Articulated Body Motion Capture by Stochastic Search

Particle filters have proven to be an effective tool for visual tracking in non-Gaussian, cluttered environments. Conventional particle filters, however, do not scale to the problem of human motion capture (HMC) because of the large number of degrees of freedom involved. Annealed Particle Filtering (APF), introduced by J. Deutscher et al. (2000), tackled this by layering the search space and was shown to be a very effective tool for HMC. We improve upon and extend the APF in two ways. First we develop a hierarchical search strategy which automatically partitions the search space without any explicit representation of the partitions. Then we introduce a crossover operator (similar to that found in genetic algorithms) which improves the ability of the tracker to search different partitions in parallel. We present results for a simple example to demonstrate the new algorithm's implementation and then apply it to the considerably more complex problem of human motion capture with 34 degrees of freedom.

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Automatic partitioning of high dimensional search spaces associated with articulated body motion capture

Vision-based full-body tracking aims to reproduce the performance of current commercial marker-based motion capture methods in a system which can be run using conventional cameras and without the use of special apparel or other equipment, improving usability in existing application domains and opening up new possibilities since the methods can be applied to image sequences acquired from any source. We present results from a system able to perform robust visual tracking with an articulated body model, using data from multiple cameras. Our approach to searching through the high-dimensional model configuration space is an algorithm called annealed particle filtering which finds the best fit to image data via multiple-layer propagation of a stochastic particle set. This algorithm efficiently searches the configuration space without the need for restrictive dynamical models, permitting tracking of agile, varied movement. The data acquired can readily be applied to the animation of CG characters. Movie files illustrating the results in this paper may be obtained from http://www.robots.ox.ac.uk/~ajd/HMC/

https://media.adelaide.edu.au/acvt/Publications/2001/2001-Markerless%20Motion%20Capture%20of%20Complex%20Full-Body%20Movement%20for%20Character%20Animation.pdf

Jonathan Deutscher

Papers

Articulated body motion capture by annealed particle filtering

Articulated Body Motion Capture by Stochastic Search

Automatic partitioning of high dimensional search spaces associated with articulated body motion capture

Markerless motion capture of complex full-body movement for character animation