MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
نویسندگان
چکیده
In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion, that extends the FLIC dataset [1] with additional motion features. We apply our architecture to this dataset and report significantly better performance than current state-of-the-art pose detection systems.
منابع مشابه
استفاده از برآورد حالت‌های پویای دست مبتنی بر مدل، برای تقلید عملکرد بازوی انسان توسط ربات با داده‌های کینکت
Pose estimation is a process to identify how a human body and/or individual limbs are configured in a given scene. Hand pose estimation is an important research topic which has a variety of applications in human-computer interaction (HCI) scenarios, such as gesture recognition, animation synthesis and robot control. However, capturing the hand motion is quite a challenging task due to its high ...
متن کاملStructure-Aware and Temporally Coherent 3D Human Pose Estimation
Deep learning methods for 3D human pose estimation from RGB images require a huge amount of domain-specific labeled data for good in-the-wild performance. However, obtaining annotated 3D pose data requires a complex motion capture setup which is generally limited to controlled settings. We propose a semi-supervised learning method using a structure-aware loss function which is able to utilize a...
متن کاملHuman Pose Estimation from Monocular Images: A Comprehensive Survey
Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we...
متن کاملConvolutional Pose Machines: A Deep Architecture for Estimating Articulated Poses
Pose Machines provide a sequential prediction framework for learning rich implicit spatial models. In this work we show a systematic design for how convolutional networks can be incorporated into the pose machine framework for learning image features and image-dependent spatial models for the task of pose estimation. The contribution of this paper is to implicitly model long-range dependencies ...
متن کامل3D Motion Data aided Human Action Recognition and Pose Estimation
In this work, we explore human action recognition and pose estimation problems. Different from traditional works of learning from 2D images or video sequences and their annotated output, we seek to solve the problems with additional 3D motion capture information, which helps to fill the gap between 2D image features and human interpretations. We first compare two different schools of approaches...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2014