Aerial human action recognition is an emerging topic in drone applications. Commercial drone platforms capable of detecting basic human actions such as hand gestures have been developed. However, a limited number of aerial video datasets are availabl...
This paper presents the simultaneous utilization of video images and inertial signals that are captured at the same time via a video camera and a wearable inertial sensor within a fusion framework in order to achieve a more robust human action recogn...
Action recognition has found extensive applications in fields such as video classification and security monitoring. However, existing action recognition methods, such as those based on 3D convolutional neural networks, often struggle to capture compr...
Action recognition is a significant and challenging topic in the field of sensor and computer vision. Two-stream convolutional neural networks (CNNs) and 3D CNNs are two mainstream deep learning architectures for video action recognition. To combine...
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...
In computer vision, human action recognition is a hot topic, popularized by the development of deep learning. Deep learning models typically accept video input without prior processing and train them to achieve recognition. However, conducting prelim...
Human action recognition is an increasingly important research topic in the fields of video sensing, analysis and understanding. Caused by unconstrained sensing conditions, there exist large intra-class variations and inter-class ambiguities in reali...
Recognizing the action of plastic bag taking from CCTV video footage represents a highly specialized and niche challenge within the broader domain of action video classification. To address this challenge, our paper introduces a novel benchmark video...
Video action recognition aims to achieve the automatic classification of human behaviors by analyzing the actions in videos, with its core lying in accurately capturing the spatial detail features of images and the temporal dynamic features among vid...
Training a model to recognize human actions in videos is computationally intensive. While modern strategies employ transfer learning methods to make the process more efficient, they still face challenges regarding flexibility and efficiency. Existing...
Conventional approaches to video action recognition perform global attention over the entire video patches, which may be ineffective due to the temporal redundancy of video frames. Recent works on masked video modeling adopt a high-ratio tube masking...
In recent years, human action recognition has been studied by many computer vision researchers. Recent studies have attempted to use two-stream networks using appearance and motion features, but most of these approaches focused on clip-level video ac...
Background: With the rise of user-generated content (UGC) platforms, we are witnessing an unprecedented surge in data. Among various content types, dance videos have emerged as a potent medium for artistic and emotional expression in the Web 2.0 era....
The primary goal of this study is to develop a deep neural network for action recognition that enhances accuracy and minimizes computational costs. In this regard, we propose a modified EMO-MoviNet-A2* architecture that integrates Evolving Normalizat...
The paper presents a comprehensive overview of intelligent video analytics and human action recognition methods. The article provides an overview of the current state of knowledge in the field of human activity recognition, including various techniqu...
Methods based on deep learning have achieved great success in the field of video action recognition. When these methods are applied to real-world scenarios that require fine-grained analysis of actions, such as being tested on a tea ceremony, limitat...
The emergence of Transformer has led to the rapid development of video understanding, but it also brings the problem of high computational complexity. Previously, there were methods to divide the feature maps into windows along the spatiotemporal dim...
Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and...
In video human action-recognition tasks, motion tempo describes the dynamic patterns and temporal scales of human motion. Different categories of actions are typically composed of sub-actions with varying motion tempos. Effectively capturing sub-acti...
Large-scale Visual-Language Models have demonstrated powerful adaptability in video recognition tasks. However, existing methods typically rely on fine-tuning or text prompt tuning. In this paper, we propose a visual-only prompting method that employ...
Modeling spatiotemporal representations is one of the most essential yet challenging issues in video action recognition. Existing methods lack the capacity to accurately model either the correlations between spatial and temporal features or the globa...
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...
Existing public domain multi-modal datasets for human action recognition only include actions of interest that have already been segmented from action streams. These datasets cannot be used to study a more realistic action recognition scenario where...
Incorporating multi-modality data is an effective way to improve action recognition performance. Based on this idea, we investigate a new data modality in which Whole-Body Keypoint and Skeleton (WKS) labels are used to capture refined body informatio...
29 September 2023
This study introduces an optimal topology of vision transformers for real-time video action recognition in a cloud-based solution. Although model performance is a key criterion for real-time video analysis use cases, inference latency plays a more cr...
Artificial intelligence’s rapid advancement has enabled various applications, including intelligent video surveillance systems, assisted living, and human–computer interaction. These applications often require one core task: video-based h...
Video object and human action detection are applied in many fields, such as video surveillance, face recognition, etc. Video object detection includes object classification and object location within the frame. Human action recognition is the detecti...
Behavioral analysis of animals in the wild plays an important role for ecological research and conservation and has been mostly performed by researchers. We introduce an action detection approach that automates this process by detecting animals and p...
In recent years, with the growth of digital media and modern imaging equipment, the use of video processing algorithms and semantic film and image management has expanded. The usage of different video datasets in training artificial intelligence algo...
Low-altitude drones, which are unimpeded by traffic congestion or urban terrain, have become a critical asset in emergency rescue missions. To address the current lack of emergency rescue data, UAV aerial videos were collected to create an experiment...
Compared with traditional methods, the action recognition model based on 3D convolutional deep neural network captures spatio-temporal features more accurately, resulting in higher accuracy. However, the large number of parameters and computational r...
Recently, Transformer-based video recognition models have achieved state-of-the-art results on major video recognition benchmarks. However, their high inference cost significantly limits research speed and practical use. In video compression, methods...
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...
Beyond traditional surveillance applications, sensor-based human action recognition and segmentation responds to a growing demand in the health and safety sector. Recently, skeletal action recognition has largely been dominated by spatio-temporal gra...
Video action recognition is vital in the research area of computer vision. In this paper, we develop a novel model, named Separable ConvNet Spatiotemporal Mixer (SCSM). Our goal is to develop an efficient and lightweight action recognition backbone t...
Different action recognition tasks exhibit significant variations in their reliance on local versus global features. Particularly for long-video understanding, dynamically balancing the contributions of both has become a critical challenge for improv...
Video action recognition based on skeleton nodes is a highlighted issue in the computer vision field. In real application scenarios, the large number of skeleton nodes and behavior occlusion problems between individuals seriously affect recognition s...
The potential benefits of recognising activities of daily living from video for active and assisted living have yet to be fully untapped. These technologies can be used for behaviour understanding, and lifelogging for caregivers and end users alike....
Research in human action recognition has accelerated significantly since the introduction of powerful machine learning tools such as Convolutional Neural Networks (CNNs). However, effective and efficient methods for incorporation of temporal informat...
The precise recognition of entire classroom meta-actions is a crucial challenge for the tailored adaptive interpretation of student behavior, given the intricacy of these actions. This paper proposes a Dynamic Position Embedding-based Model for Stude...
Due to its capacity to gather vast, high-level data about human activity from wearable or stationary sensors, human activity recognition substantially impacts people’s day-to-day lives. Multiple people and things may be seen acting in the video...
Secondary actions in vehicles are activities that drivers engage in while driving that are not directly related to the primary task of operating the vehicle. Secondary Action Recognition (SAR) in drivers is vital for enhancing road safety and minimiz...
Interest in utilizing neural networks in a variety of scientific and academic studies and in industrial applications is increasing. In addition to the growing interest in neural networks, there is also a rising interest in video classification. Objec...
Human action recognition (HAR) as the most representative human-centred computer vision task is critical in human resource management (HRM), especially in human resource recruitment, performance appraisal, and employee training. Currently, prevailing...
This paper focuses on image and video content analysis of handball scenes and applying deep learning methods for detecting and tracking the players and recognizing their activities. Handball is a team sport of two teams played indoors with the ball w...
Our approach to action recognition is grounded in the intrinsic coexistence of and complementary relationship between audio and visual information in videos. Going beyond the traditional emphasis on visual features, we propose a transformer-based net...
Human Action Recognition has seen significant advances through transformer-based architectures, yet achieving a nuanced understanding often requires fusing multiple data modalities. Standard models relying solely on RGB video can struggle with action...
Most current deep learning models are suboptimal in terms of the flexibility of their input shape. Usually, computer vision models only work on one fixed shape used during training, otherwise their performance degrades significantly. For video-relate...
Recognizing human actions through video analysis has gained significant attention in applications like surveillance, sports analytics, and human–computer interaction. While deep learning models such as 3D convolutional neural networks (CNNs) an...
The increasing demand for surveillance systems has resulted in an unprecedented rise in the volume of video data being generated daily. The volume and frequency of the generation of video streams make it both impractical as well as inefficient to man...
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