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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

Derived from rapid advances in computer vision and machine learning, video analysis tasks have been moving from inferring the present state to predicting the future state. Vision-based action recognition and prediction from videos are such tasks, where action recognition is to infer human actions (present state) based upon complete action executions, and action prediction to predict human actions (future state) based upon incomplete action executions. These two tasks have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as visual surveillance, autonomous driving vehicle, entertainment, and video retrieval, etc. Many attempts have been devoted in the last a few decades in order to build a robust and effective framework for action recognition and prediction. In this paper, we survey the complete state-of-the-art techniques in the action recognition and prediction. Existing models, popular algorithms, technical difficulties, popular action databases, evaluation protocols, and promising future directions are also provided with systematic discussions.

Human Action Recognition and Prediction: A Survey.

Three-dimensional hand gesture recognition has attracted increasing research interests in computer vision, pattern recognition, and human-computer interaction. The emerging depth sensors greatly inspired various hand gesture recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand gesture recognition using 3D depth sensors. We first review the commercial depth sensors and public data sets that are widely used in this field. Then, we review the state-of-the-art research for 3D hand gesture recognition in four aspects: 1) 3D hand modeling; 2) static hand gesture recognition; 3) hand trajectory gesture recognition; and 4) continuous hand gesture recognition. While the emphasis is on 3D hand gesture recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.

Survey on 3D Hand Gesture Recognition

https://ro.uow.edu.au/cgi/viewcontent.cgi?article=7172&context=eispapers

RGB-D-based action recognition datasets

https://liris.cnrs.fr/Documents/Liris-6807.pdf

Evaluation of video activity localizations integrating quality and quantity measurements

We propose a new method for human pose estimation which leverages information from multiple views to impose a strong prior on articulated pose. The novelty of the method concerns the types of coherence modelled. Consistency is maximised over the different views through different terms modelling classical geometric information (coherence of the resulting poses) as well as appearance information which is modelled as latent variables in the global energy function. Moreover, adequacy of each view is assessed and their contributions are adjusted accordingly. Experiments on the HumanEva and Utrecht multi-person motion datasets show that the proposed method significantly decreases the estimation error compared to single-view results.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cvi.2017.0146

Multi-view pose estimation with mixtures-of-parts and adaptive viewpoint selection

We propose a new method for activity recognition based on a view independent representation of human motion. Robust 3D volume motion templates (VMTs) are calculated from tracklets. View independence is achieved through a rotation with respect to a canonical orientation. From this volumes, features based on 3D gradients are extracted, projected to a codebook and pooled into a bags-of-words model classified with an SVM classifier. Experiments show that the method outperforms the original HoG3D method.

Activity recognition with volume motion templates and histograms of 3D gradients

L'estimation de la pose humaine et la reconnaissance des activites humaines sont des etapes importantes dans de nombreuses applications comme la robotique, la surveillance et la securite, etc. Actuellement abordees dans le domaine, ces tâches ne sont toujours pas resolues dans des environnements non-cooperatifs particulierement. Ces tâches admettent de divers defis comme l'occlusion, les variations des vetements, etc. Les methodes qui exploitent des images de profondeur ont l’avantage concernant les defis lies a l'arriere-plan et a l'apparence, pourtant, l’application est limitee pour des raisons materielles. Dans un premier temps, nous nous sommes concentres sur la reconnaissance des actions complexes depuis des videos. Pour ceci, nous avons introduit une representation spatio-temporelle independante du point de vue. Plus precisement, nous avons capture le mouvement de la personne en utilisant un capteur de profondeur et l'avons encode en 3D pour le representer. Un descripteur 3D a ensuite ete utilise pour la classification des sequences avec la methodologie bag-of-words. Pour la deuxieme partie, notre objectif etait l'estimation de pose articulee, qui est souvent une etape intermediaire pour la reconnaissance de l'activite. Notre motivation etait d'incorporer des informations a partir de capteurs multiples et de les fusionner pour surmonter le probleme de l'auto-occlusion. Ainsi, nous avons propose un modele de flexible mixtures-of-parts multi-vues inspire par la methodologie classique de structure pictural. Nous avons demontre que les contraintes geometriques et les parametres de coherence d'apparence sont efficaces pour renforcer la coherence entre les points de vue, aussi que les parametres classiques. Finalement, nous avons evalue ces nouvelles methodes sur des datasets publics, qui verifie que l'utilisation de representations independantes de la vue et l'integration d'informations a partir de points de vue multiples ameliore la performance pour les tâches ciblees dans le cadre de cette manuscrit.

https://tel.archives-ouvertes.fr/tel-01921842/document

Emre Dogan

Papers

Evaluation of video activity localizations integrating quality and quantity measurements

Multi-view pose estimation with mixtures-of-parts and adaptive viewpoint selection

Activity recognition with volume motion templates and histograms of 3D gradients

Estimation de pose humaine et reconnaissance d’action par un système multi-robots

Human pose estimation and action recognition by multi-robot systems