17,496 research outputs found
Exploiting deep residual networks for human action recognition from skeletal data
The computer vision community is currently focusing on solving action recognition problems in real videos, which contain thousands of samples with many challenges. In this process, Deep Convolutional Neural Networks (D-CNNs) have played a significant role in advancing the state-of-the-art in various vision-based action recognition systems. Recently, the introduction of residual connections in conjunction with a more traditional CNN model in a single architecture called Residual Network (ResNet) has shown impressive performance and great potential for image recognition tasks. In this paper, we investigate and apply deep ResNets for human action recognition using skeletal data provided by depth sensors. Firstly, the 3D coordinates of the human body joints carried in skeleton sequences are transformed into image-based representations and stored as RGB images. These color images are able to capture the spatial-temporal evolutions of 3D motions from skeleton sequences and can be efficiently learned by D-CNNs. We then propose a novel deep learning architecture based on ResNets to learn features from obtained color-based representations and classify them into action classes. The proposed method is evaluated on three challenging benchmark datasets including MSR Action 3D, KARD, and NTU-RGB+D datasets. Experimental results demonstrate that our method achieves state-of-the-art performance for all these benchmarks whilst requiring less computation resource. In particular, the proposed method surpasses previous approaches by a significant margin of 3.4% on MSR Action 3D dataset, 0.67% on KARD dataset, and 2.5% on NTU-RGB+D dataset
Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks
This paper proposes three simple, compact yet effective representations of
depth sequences, referred to respectively as Dynamic Depth Images (DDI),
Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images
(DDMNI). These dynamic images are constructed from a sequence of depth maps
using bidirectional rank pooling to effectively capture the spatial-temporal
information. Such image-based representations enable us to fine-tune the
existing ConvNets models trained on image data for classification of depth
sequences, without introducing large parameters to learn. Upon the proposed
representations, a convolutional Neural networks (ConvNets) based method is
developed for gesture recognition and evaluated on the Large-scale Isolated
Gesture Recognition at the ChaLearn Looking at People (LAP) challenge 2016. The
method achieved 55.57\% classification accuracy and ranked place in
this challenge but was very close to the best performance even though we only
used depth data.Comment: arXiv admin note: text overlap with arXiv:1608.0633
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays advanced solutions that can learn from millions of
videos and apply to almost all daily activities. Given the broad range of
applications from video surveillance to human-computer interaction, scientific
milestones in action recognition are achieved more rapidly, eventually leading
to the demise of what used to be good in a short time. This motivated us to
provide a comprehensive review of the notable steps taken towards recognizing
human actions. To this end, we start our discussion with the pioneering methods
that use handcrafted representations, and then, navigate into the realm of deep
learning based approaches. We aim to remain objective throughout this survey,
touching upon encouraging improvements as well as inevitable fallbacks, in the
hope of raising fresh questions and motivating new research directions for the
reader
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
- …