Multi-view action recognition has gained a great interest in video surveillance, human computer interaction, and multimedia retrieval, where multiple cameras of different types are deployed to provide a complementary field of views. Fusion of multipl...
Spatiotemporal and motion feature representations are the key to video action recognition. Typical previous approaches are to utilize 3D CNNs to cope with both spatial and temporal features, but they suffer from huge computations. Other approaches ar...
Skeleton-based action recognition is an important subject in deep learning. Graph Convolutional Networks (GCNs) have demonstrated strong performance by modeling the human skeleton as a natural topological graph, representing the connections between j...
Multi-view data, captured from various perspectives, is crucial for training view-invariant human action recognition models, yet its acquisition is hindered by spatio-temporal constraints and high costs. This study aims to develop the Pose Scene Ever...
Due to progress and demographic change, society is facing a crucial challenge related to increased life expectancy and a higher number of people in situations of dependency. As a consequence, there exists a significant demand for support systems for...
Human–Robot Interaction (HRI) depends on robust perception systems that enable intuitive and seamless interaction between humans and robots. This work introduces a multi-view perception framework designed for HRI, incorporating object detection...
This paper proposes an action recognition framework for depth map sequences using the 3D Space-Time Auto-Correlation of Gradients (STACOG) algorithm. First, each depth map sequence is split into two sets of sub-sequences of two different frame length...
Human Action Recognition (HAR) is a branch of computer vision that deals with the identification of human actions at various levels including low level, action level, and interaction level. Previously, a number of HAR algorithms have been proposed ba...
Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long S...
Cattle behavior recognition is essential for monitoring their health and welfare. Existing techniques for behavior recognition in closed barns typically rely on direct observation to detect changes using wearable devices or surveillance cameras. Whil...
In this paper, we propose a viewpoint-aware action recognition method using skeleton-based features from static images. Our method consists of three main steps. First, we categorize the viewpoint from an input static image. Second, we extract 2D/3D j...
The paper presents a novel hybrid network for large-scale action recognition from multiple modalities. The network is built upon the proposed weighted dynamic images. It effectively leverages the strengths of the emerging Convolutional Neural Network...
Depth video sequence-based deep models for recognizing human actions are scarce compared to RGB and skeleton video sequences-based models. This scarcity limits the research advancements based on depth data, as training deep models with small-scale da...
Human action recognition (HAR) has gained significant attention recently as it can be adopted for a smart surveillance system in Multimedia. However, HAR is a challenging task because of the variety of human actions in daily life. Various solutions b...
One of the most important research topics nowadays is human action recognition, which is of significant interest to the computer vision and machine learning communities. Some of the factors that hamper it include changes in postures and shapes and th...
With the increasing demand on the usage of smart and networked cameras in intelligent and ambient technology environments, development of algorithms for such resource-distributed networks are of great interest. Multi-view action recognition addresses...
Inverse synthetic aperture radar (ISAR) three-dimensional (3D) imaging technology enables the acquisition of clear 3D structures of targets, significantly enhancing target recognition performance. In resource-constrained environments, an effective re...
This paper presents a novel feature descriptor for multiview human action recognition. This descriptor employs the region-based features extracted from the human silhouette. To achieve this, the human silhouette is divided into regions in a radial fa...
RGB and depth modalities contain more abundant and interactive information, and convolutional neural networks (ConvNets) based on multi-modal data have achieved successful progress in action recognition. Due to the limitation of a single stream, it i...
Most approaches to action recognition based on pseudo-images involve encoding skeletal data into RGB-like image representations. This approach cannot fully exploit the kinematic features and structural information of human poses, and convolutional ne...
Multi-player action recognition for automatic analysis in sports is the subject of increasing attention. Trajectory-tracking technology is key for accurate recognition, but little research has focused on this aspect, especially for non-professional m...
Designing motion representations for 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant to viewpoint changes and result in a good performance with...
Obtaining discriminative joint features is crucial for skeleton-based human action recognition. Current models mainly focus on the research of skeleton topology encoding. However, their predefined topology is the same and fixed for all action samples...
Pedestrian behavior recognition in the driving environment is an important technology to prevent pedestrian accidents by predicting the next movement. It is necessary to recognize current pedestrian behavior to predict future pedestrian behavior. How...
In this paper, we describe how information obtained from multiple views usinga network of cameras can be effectively combined to yield a reliable and fast humanactivity recognition system. First, we present a score-based fusion technique for combinin...
Human action recognition (HAR) has emerged as a core research domain for video understanding and analysis, thus attracting many researchers. Although significant results have been achieved in simple scenarios, HAR is still a challenging task due to i...
Action prediction is an important task in human activity analysis, which has many practical applications, such as human–robot interactions and autonomous driving. Action prediction often comprises two subtasks: action semantic prediction and fu...
For unmanned aerial vehicle (UAV) ground crew marshalling tasks, the accuracy of skeleton-based action recognition is often limited by the high similarity of motion patterns across action categories as well as variations in individual performance. To...
With the accelerating pace of urbanization, historic districts are increasingly confronted with the dual challenge of coordinating heritage preservation and sustainable development. This study proposes an intelligent evaluation framework that integra...
Affective computing is an increasingly important outgrowth of Artificial Intelligence, which is intended to deal with rich and subjective human communication. In view of the complexity of affective expression, discriminative feature extraction and co...
12 December 2024
Accurate human action recognition is becoming increasingly important across various fields, including healthcare and self-driving cars. A simple approach to enhance model performance is incorporating additional data modalities, such as depth frames,...
Floor positioning is an important aspect of indoor positioning technology, which is closely related to location-based services (LBSs). Currently, floor positioning technologies are mainly based on radio signals and barometric pressure. The former are...