Mining Key Skeleton Poses with Latent SVM for Action Recognition
نویسندگان
چکیده
منابع مشابه
Silhouette-based human action recognition using sequences of key poses
In this paper, a human action recognition method is presented in which pose representation is based on the contour points of the human silhouette and actions are learned by making use of sequences of multi-view key poses. Our contribution is two-fold. Firstly, our approach achieves state-of-the-art success rates without compromising the speed of the recognition process and therefore showing sui...
متن کاملView-Invariant Action Recognition Using Latent Kernelized Structural SVM
This paper goes beyond recognizing human actions from a fixed view and focuses on action recognition from an arbitrary view. A novel learning algorithm, called latent kernelized structural SVM, is proposed for the view-invariant action recognition, which extends the kernelized structural SVM framework to include latent variables. Due to the changing and frequently unknown positions of the camer...
متن کاملKernel Latent SVM for Visual Recognition
Latent SVMs (LSVMs) are a class of powerful tools that have been successfully applied to many applications in computer vision. However, a limitation of LSVMs is that they rely on linear models. For many computer vision tasks, linear models are suboptimal and nonlinear models learned with kernels typically perform much better. Therefore it is desirable to develop the kernel version of LSVM. In t...
متن کاملAn Efficient Approach for Multi-view Human Action Recognition Based on Bag-of-Key-Poses
This paper presents a novel multi-view human action recognition approach based on a bag-of-key-poses. In the case of multi-view scenarios, it is especially difficult to perform accurate action recognition that still runs at an admissible recognition speed. The presented method aims to fill this gap by combining a silhouette-based pose representation with a simple, yet effective multi-view learn...
متن کاملLatent Boosting for Action Recognition
In this paper we present LatentBoost, a novel learning algorithm for training models with latent variables in a boosting framework. This algorithm allows for training of structured latent variable models with boosting. The popular latent SVM framework allows for training of models with structured latent variables in a max-margin framework. LatentBoost provides an analogous capability for boosti...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Computational Intelligence and Soft Computing
سال: 2017
ISSN: 1687-9724,1687-9732
DOI: 10.1155/2017/5861435