Sparse B-spline polynomial descriptors for human activity recognition

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Sparse B-spline polynomial descriptors for human activity recognition

Article history: Received 19 May 2008 Received in revised form 10 April 2009 Accepted 16 May 2009

متن کامل

Human Gesture Recognition using Sparse B-spline Polynomial Representations

The extraction and quantization of local image and video descriptors for the subsequent creation of visual codebooks is a technique that has proved extremely effective for image and video retrieval applications. In this paper we build on this concept and extract a new set of visual descriptors that are derived from spatiotemporal salient points detected on given image sequences and provide loca...

متن کامل

Building Unified Human Descriptors For Multi-Type Activity Recognition

Activity recognition is an important as well as a difficult task in computer vision. In the past years many types of activities – single actions, two persons interactions or ego-centric activities to name a few – have been analyzed. Nevertheless, researchers have always treated such types of activities separately. In this paper, we propose a new problem: labeling a complex scene where activitie...

متن کامل

Non-polynomial Spline Method for Solving Coupled Burgers Equations

In this paper, non-polynomial spline method for solving Coupled Burgers Equations are presented. We take a new spline function. The stability analysis using Von-Neumann technique shows the scheme is unconditionally stable. To test accuracy the error norms 2L, L are computed and give two examples to illustrate the sufficiency of the method for solving such nonlinear partial differential equation...

متن کامل

Sparse Spatiotemporal Coding for Activity Recognition

We present a new approach to learning sparse, spatiotemporal features and demonstrate the utility of the approach by applying the resulting sparse codes to the problem of activity recognition. Learning features that discriminate among human activities in video is difficult in part because the stable space-time events that reliably characterize the relevant motions are rare. To overcome this pro...

متن کامل

ذخیره 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: Image and Vision Computing

سال: 2009

ISSN: 0262-8856

DOI: 10.1016/j.imavis.2009.05.010