Papers Multimodal Activity Recognition
“Multimodal Activity Recognition” 태그가 달린 논문 30편 · 필터 해제
MuMu: Cooperative Multitask Learning-based Guided Multimodal Fusion
Multimodal sensors (visual, non-visual, and wearable) can provide complementary information to develop robust perception systems for recognizing activities accurately. However, it is challenging to extract robust multimo…
Activity RecognitionHuman Activity RecognitionMultimodal Activity RecognitionOPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based Sensors
This paper presents a comprehensive dataset intended to evaluate passive Human Activity Recognition (HAR) and localization techniques with measurements obtained from synchronized Radio-Frequency (RF) devices and vision-b…
Activity RecognitionHuman Activity RecognitionMultimodal Activity RecognitionFusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks
In this paper, we present Fusion-GCN, an approach for multimodal action recognition using Graph Convolutional Networks (GCNs). Action recognition methods based around GCNs recently yielded state-of-the-art performance fo…
Action RecognitionMultimodal Activity RecognitionSkeleton Based Action RecognitionDistilling Audio-Visual Knowledge by Compositional Contrastive Learning
Having access to multi-modal cues (e.g. vision and audio) empowers some cognitive tasks to be done faster compared to learning from a single modality. In this work, we propose to transfer knowledge across heterogeneous m…
Audio Taggingaudio-visual learningContrastive LearningKnowledge Distillation+3Multi-GAT: A Graphical Attention-based Hierarchical Multimodal Representation Learning Approach for Human Activity Recognition
Recognizing human activities is one of the crucial capabilities that a robot needs to have to be useful around people. Although modern robots are equipped with various types of sensors, human activity recognition (HAR) s…
Activity RecognitionHuman Activity RecognitionMixture-of-ExpertsMultimodal Activity Recognition+1Gimme Signals: Discriminative signal encoding for multimodal activity recognition
We present a simple, yet effective and flexible method for action recognition supporting multiple sensor modalities. Multivariate signal sequences are encoded in an image and are then classified using a recently proposed…
Action RecognitionActivity RecognitionMultimodal Activity RecognitionSkeleton Based Action RecognitionMMAct: A Large-Scale Dataset for Cross Modal Human Action Understanding
Unlike vision modalities, body-worn sensors or passive sensing can avoid the failure of action understanding in vision related challenges, e.g. occlusion and appearance variation. However, a standard large-scale dataset …
Action RecognitionAction UnderstandingMultimodal Activity RecognitionTransfer LearningActivity recognition using ST-GCN with 3D motion data
For the Nurse Care Activity Recognition Challenge, an activity recognition algorithm was developed by Team TDU-DSML. A spatial-temporal graph convolutional network (ST-GCN) was applied to process 3D motion capture data i…
Activity RecognitionMultimodal Activity RecognitionTime SeriesTime Series AnalysisNurse care activity recognition challenge: summary and results
Although activity recognition has been studied for a long time now, research and applications have focused on physical activity recognition. Even if many application domains require the recognition of more complex activi…
Activity RecognitionMultimodal Activity RecognitionCan a simple approach identify complex nurse care activity?
For the last two decades, more and more complex methods have been developed to identify human activities using various types of sensors, e.g., data from motion capture, accelerometer, and gyroscopes sensors. To date, mos…
Activity RecognitionMultimodal Activity RecognitionBayesian Hierarchical Dynamic Model for Human Action Recognition
Human action recognition remains as a challenging task partially due to the presence of large variations in the execution of action. To address this issue, we propose a probabilistic model called Hierarchical Dynamic Mod…
Action RecognitionBayesian InferenceMissing Valuesmodel+3AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
Learning to represent videos is a very challenging task both algorithmically and computationally. Standard video CNN architectures have been designed by directly extending architectures devised for image understanding to…
Action ClassificationAction RecognitionMultimodal Activity RecognitionOptical Flow Estimation+2Autonomous Human Activity Classification from Ego-vision Camera and Accelerometer Data
There has been significant amount of research work on human activity classification relying either on Inertial Measurement Unit (IMU) data or data from static cameras providing a third-person view. Using only IMU data li…
General ClassificationMultimodal Activity RecognitionEV-Action: Electromyography-Vision Multi-Modal Action Dataset
Multi-modal human action analysis is a critical and attractive research topic. However, the majority of the existing datasets only provide visual modalities (i.e., RGB, depth and skeleton). To make up this, we introduce …
Action AnalysisAction RecognitionElectromyography (EMG)Multimodal Activity Recognition+1STAR-Net: Action Recognition using Spatio-Temporal Activation Reprojection
While depth cameras and inertial sensors have been frequently leveraged for human action recognition, these sensing modalities are impractical in many scenarios where cost or environmental constraints prohibit their use.…
Action RecognitionMultimodal Activity RecognitionPose EstimationSkeleton Based Action Recognition+1Adaptive Feature Processing for Robust Human Activity Recognition on a Novel Multi-Modal Dataset
Human Activity Recognition (HAR) is a key building block of many emerging applications such as intelligent mobility, sports analytics, ambient-assisted living and human-robot interaction. With robust HAR, systems will be…
Activity RecognitionAutonomous VehiclesBIG-bench Machine LearningHuman Activity Recognition+2Cross-modal Learning by Hallucinating Missing Modalities in RGB-D Vision
Diverse input data modalities can provide complementary cues for several tasks, usually leading to more robust algorithms and better performance. However, while a (training) dataset could be accurately designed to includ…
Action RecognitionHallucinationMultimodal Activity RecognitionSkeleton Based Action Recognition+1Action Machine: Rethinking Action Recognition in Trimmed Videos
Existing methods in video action recognition mostly do not distinguish human body from the environment and easily overfit the scenes and objects. In this work, we present a conceptually simple, general and high-performan…
Action RecognitionMultimodal Activity RecognitionPose EstimationSkeleton Based Action Recognition+1Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference
Deep neural networks (DNNs) provide state-of-the-art results for a multitude of applications, but the approaches using DNNs for multimodal audiovisual applications do not consider predictive uncertainty associated with i…
Activity RecognitionBayesian InferenceMultimodal Activity RecognitionVariational InferenceRecognizing Human Actions as the Evolution of Pose Estimation Maps
Most video-based action recognition approaches choose to extract features from the whole video to recognize actions. The cluttered background and non-action motions limit the performances of these methods, since they lac…
Action RecognitionMultimodal Activity RecognitionPose EstimationSkeleton Based Action Recognition+1