Evaluation of a skeleton-based method for human activity recognition on a large-scale RGB-D dataset
نویسندگان
چکیده
Low cost RGB-D sensors have been used extensively in the field of Human Action Recognition. The availability of skeleton joints simplifies the process of feature extraction from depth or RGB frames, and this feature fostered the development of activity recognition algorithms using skeletons as input data. This work evaluates the performance of a skeleton-based algorithm for Human Action Recognition on a large-scale dataset. The algorithm exploits the bag of key poses method, where a sequence of skeleton features is represented as a set of key poses. A temporal pyramid is adopted to model the temporal structure of the key poses, represented using histograms. Finally, a multi-class SVM performs the classification task, obtaining promising results on the large-scale NTU RGB+D dataset.
منابع مشابه
Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition
It remains a challenge to efficiently extract spatialtemporal information from skeleton sequences for 3D human action recognition. Although most recent action recognition methods are based on Recurrent Neural Networks which present outstanding performance, one of the shortcomings of these methods is the tendency to overemphasize the temporal information. Since 3D convolutional neural network(3D...
متن کاملExploiting 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 c...
متن کاملPKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding
Despite the fact that many 3D human activity benchmarks being proposed, most existing action datasets focus on the action recognition tasks for the segmented videos. There is a lack of standard large-scale benchmarks, especially for current popular data-hungry deep learning based methods. In this paper, we introduce a new large scale benchmark (PKU-MMD) for continuous multi-modality 3D human ac...
متن کاملA 3D Human Skeletonization Algorithm for a Single Monocular Camera Based on Spatial–Temporal Discrete Shadow Integration
Three-dimensional (3D) human skeleton extraction is a powerful tool for activity acquirement and analyses, spawning a variety of applications on somatosensory control, virtual reality and many prospering fields. However, the 3D human skeletonization relies heavily on RGB-Depth (RGB-D) cameras, expensive wearable sensors and specific lightening conditions, resulting in great limitation of its ou...
متن کاملAn evaluation of 3D motion flow and 3D pose estimation for human action recognition
Modern human action recognition algorithms which exploit 3D information mainly classify video sequences by extracting local or global features from the RGB-D domain or classifying the skeleton information provided by a skeletal tracker. In this paper, we propose a comparison between two techniques which share the same classification process, while differing in the type of descriptor which is cl...
متن کامل