From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

http://pancamr.lecture.ub.ac.id/files/2012/02/A-survey-on-vision-based-human-action-recognition_Image-and-Vision-Computing_2010.pdf

A survey on vision-based human action recognition

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 .

/pdf/human3-6m-large-scale-datasets-and-predictive-methods-for-3d-3g2xlb8fic.pdf

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

The past decade has witnessed a rapid proliferation of video cameras in all walks of life and has resulted in a tremendous explosion of video content. Several applications such as content-based video annotation and retrieval, highlight extraction and video summarization require recognition of the activities occurring in the video. The analysis of human activities in videos is an area with increasingly important consequences from security and surveillance to entertainment and personal archiving. Several challenges at various levels of processing-robustness against errors in low-level processing, view and rate-invariant representations at midlevel processing and semantic representation of human activities at higher level processing-make this problem hard to solve. In this review paper, we present a comprehensive survey of efforts in the past couple of decades to address the problems of representation, recognition, and learning of human activities from video and related applications. We discuss the problem at two major levels of complexity: 1) "actions" and 2) "activities." "Actions" are characterized by simple motion patterns typically executed by a single human. "Activities" are more complex and involve coordinated actions among a small number of humans. We will discuss several approaches and classify them according to their ability to handle varying degrees of complexity as interpreted above. We begin with a discussion of approaches to model the simplest of action classes known as atomic or primitive actions that do not require sophisticated dynamical modeling. Then, methods to model actions with more complex dynamics are discussed. The discussion then leads naturally to methods for higher level representation of complex activities.

/pdf/machine-recognition-of-human-activities-a-survey-4jrl67n0jj.pdf

Machine Recognition of Human Activities: A Survey

One way that artists create compelling character animations is by manipulating details of a character's motion. This process is expensive and repetitive. We show that we can make such motion editing more efficient by generalizing the edits an animator makes on short sequences of motion to other sequences. Our method predicts frames for the motion using Gaussian process models of kinematics and dynamics. These estimates are combined with probabilistic inference. Our method can be used to propagate edits from examples to an entire sequence for an existing character, and it can also be used to map a motion from a control character to a very different target character. The technique shows good generalization. For example, we show that an estimator, learned from a few seconds of edited example animation using our methods, generalizes well enough to edit minutes of character animation in a high-quality fashion. Learning is interactive: An animator who wants to improve the output can provide small, correcting examples and the system will produce improved estimates of motion. We make this interactive learning process efficient and natural with a fast, full-body IK system with novel features. Finally, we present data from interviews with professional character animators that indicate that generalizing and propagating animator edits can save artists significant time and work.

Generalizing motion edits with Gaussian processes

We review methods for kinematic tracking of the human body in video. The review is part of a projected book that is intended to cross-fertilize ideas about motion representation between the animation and computer vision communities. The review confines itself to the earlier stages of motion, focusing on tracking and motion synthesis; future material will cover activity representation and motion generation.

http://www.nowpublishers.com/article/DownloadSummary/CGV-005

Computational Studies of Human Motion

Footskate, where a character's foot slides on the ground when it should be planted firmly, is a common artifact resulting from almost any attempt to modify motion capture data. We describe an online method for fixing footskate that requires no manual clean-up. An important part of fixing footskate is determining when the feet should be planted. We introduce an oracle that can automatically detect when foot plants should occur. Our method is more accurate than baseline methods that check the height or speed of the feet. These baseline methods perform especially poorly on noisy or imperfect data, requiring manual fixing. Once trained, our oracle is robust and can be used without manual clean-up, making it suitable for large databases of motion. After the foot plants are detected we use an off-the-shelf inverse kinematics based method to maintain ground contact during each foot plant. Our foot plant detection mechanism coupled with an IK based fixer can be treated as a black box that produces natural-looking motion of the feet, making it suitable for interactive systems. We demonstrate several applications which would produce unrealistic motion without our method.

http://luthuli.cs.uiuc.edu/~daf/papers/p49-ikemoto.pdf

Knowing when to put your foot down

This paper describes a method that can significantly increase the size of a collection of motion observations by cutting limbs from one motion sequence and attaching them to another. Not all such transplants are successful, because correlations across the body are a significant feature of human motion. The method uses randomized search based around a set of rules to generate transplants that are (a) likely to be successful and (b) likely to enrich the existing motion collection. The resulting frames are annotated by a classifier to tell whether they look like human motion or not.We evaluate the method by obtaining motion demands from an application, synthesizing motions to meet those demands, and then scoring the synthesized motions. Motions synthesized using transplants are generally somewhat better than those synthesized without using transplants, because transplanting generates many frames quite close to the original frames, so that it is easier for the motion synthesis process to find a good path in the motion graph. Furthermore, we show classifier errors tend to have relatively little impact in practice.Finally, we show that transplanted motion data can be used to synthesize motions of a group coordinated in space and time without producing motions that share frames.

/pdf/enriching-a-motion-collection-by-transplanting-limbs-4rglxcnz9o.pdf

Enriching a motion collection by transplanting limbs

We describe a discriminative method for distinguishing natural-looking from unnatural-looking motion. Our method is based on physical and data-driven features of motion to which humans seem sensitive. We demonstrate that our technique is significantly more accurate than current alternatives.We use this technique as the testing part of a hypothesize-and-test motion synthesis procedure. The mechanism we build using this procedure can quickly provide an application with a transition of user-specified duration from any frame in a motion collection to any other frame in the collection. During pre-processing, we search all possible 2-, 3-, and 4-way blends between representative samples of motion obtained using clustering. The blends are automatically evaluated, and the recipe (i.e., the representatives and the set of weighting functions) that created the best blend is cached.At run-time, we build a transition between motions by matching a future window of the source motion to a representative, matching the past of the target motion to a representative, and then applying the blend recipe recovered from the cache to source and target motion. People seem sensitive to poor contact with the environment like sliding foot plants. We determine appropriate temporal and positional constraints for each foot plant using a novel technique, then apply an off-the-shelf inverse kinematics technique to enforce the constraints. This synthesis procedure yields good-looking transitions between distinct motions with very low online cost.

/pdf/quick-transitions-with-cached-multi-way-blends-1qxh4aroq2.pdf

Leslie Ikemoto

Papers

Generalizing motion edits with Gaussian processes

Computational Studies of Human Motion

Knowing when to put your foot down

Enriching a motion collection by transplanting limbs

Quick transitions with cached multi-way blends