18 research outputs found
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn
This paper presents an image classification based approach for skeleton-based
video action recognition problem. Firstly, A dataset independent
translation-scale invariant image mapping method is proposed, which transformes
the skeleton videos to colour images, named skeleton-images. Secondly, A
multi-scale deep convolutional neural network (CNN) architecture is proposed
which could be built and fine-tuned on the powerful pre-trained CNNs, e.g.,
AlexNet, VGGNet, ResNet etal.. Even though the skeleton-images are very
different from natural images, the fine-tune strategy still works well. At
last, we prove that our method could also work well on 2D skeleton video data.
We achieve the state-of-the-art results on the popular benchmard datasets e.g.
NTU RGB+D, UTD-MHAD, MSRC-12, and G3D. Especially on the largest and challenge
NTU RGB+D, UTD-MHAD, and MSRC-12 dataset, our method outperforms other methods
by a large margion, which proves the efficacy of the proposed method
Computer Vision and Image Processing: A Paper Review
Computer vision has been studied from many persective. It expands from raw data recording into techniques and ideas combining digital image processing, pattern recognition, machine learning and computer graphics. The wide usage has attracted many scholars to integrate with many disciplines and fields. This paper provide a survey of the recent technologies and theoretical concept explaining the development of computer vision especially related to image processing using different areas of their field application. Computer vision helps scholars to analyze images and video to obtain necessary information,   understand information on events or descriptions, and scenic pattern. It used method of multi-range application domain with massive data analysis. This paper provides contribution of recent development on reviews related to computer vision, image processing, and their related studies. We categorized the computer vision mainstream into four group e.g., image processing, object recognition, and machine learning. We also provide brief explanation on the up-to-date information about the techniques and their performance
Adaptive Graphical Model Network for 2D Handpose Estimation
In this paper, we propose a new architecture called Adaptive Graphical Model
Network (AGMN) to tackle the task of 2D hand pose estimation from a monocular
RGB image. The AGMN consists of two branches of deep convolutional neural
networks for calculating unary and pairwise potential functions, followed by a
graphical model inference module for integrating unary and pairwise potentials.
Unlike existing architectures proposed to combine DCNNs with graphical models,
our AGMN is novel in that the parameters of its graphical model are conditioned
on and fully adaptive to individual input images. Experiments show that our
approach outperforms the state-of-the-art method used in 2D hand keypoints
estimation by a notable margin on two public datasets.Comment: 30th British Machine Vision Conference (BMVC