SOTAVerified

Activity Recognition

Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.

Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters

Papers

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Showing 401–450 of 1322 papers

TitleStatusHype
Transfer Learning in Human Activity Recognition: A Survey—0
Federated Unlearning for Human Activity Recognition—0
DOO-RE: A dataset of ambient sensors in a meeting room for activity recognition—0
Self-supervised New Activity Detection in Sensor-based Smart Environments—0
Dense Optical Flow Estimation Using Sparse Regularizers from Reduced Measurements—0
Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey—0
Multi-objective Feature Selection in Remote Health Monitoring Applications—0
Standardizing Your Training Process for Human Activity Recognition Models: A Comprehensive Review in the Tunable Factors—0
TRTAR: Transmissive RIS-assisted Through-the-wall Human Activity Recognition—0
Group Activity Recognition using Unreliable Tracked Pose—0
Balancing Continual Learning and Fine-tuning for Human Activity Recognition—0
CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition—0
Bi-Causal: Group Activity Recognition via Bidirectional Causality—0
Directional Antenna Systems for Long-Range Through-Wall Human Activity RecognitionCode0
Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity RecognitionCode0
Acceleration Estimation of Signal Propagation Path Length Changes for Wireless Sensing—0
Device-Free Human State Estimation using UWB Multi-Static Radios—0
Pose2Gaze: Eye-body Coordination during Daily Activities for Gaze Prediction from Full-body Poses—0
ST(OR)2: Spatio-Temporal Object Level Reasoning for Activity Recognition in the Operating Room—0
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning—0
Enhanced Spatio- Temporal Image Encoding for Online Human Activity RecognitionCode0
Towards a geometric understanding of Spatio Temporal Graph Convolution NetworksCode0
CSI-Based Cross-Domain Activity Recognition via Zero-Shot Prototypical Networks—0
A Review of Machine Learning Methods Applied to Video Analysis Systems—0
The Potential of Vision-Language Models for Content Moderation of Children's Videos—0
Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series—0
From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safety—0
A Causality-Aware Pattern Mining Scheme for Group Activity Recognition in a Pervasive Sensor Space—0
Student Activity Recognition in Classroom Environments using Transfer Learning—0
Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition—0
REACT: Recognize Every Action Everywhere All At Once—0
Contrastive Left-Right Wearable Sensors (IMUs) Consistency Matching for HAR—0
Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes—0
Passive Human Sensing Enhanced by Reconfigurable Intelligent Surface: Opportunities and Challenges—0
TENT: Connect Language Models with IoT Sensors for Zero-Shot Activity Recognition—0
MVSA-Net: Multi-View State-Action Recognition for Robust and Deployable Trajectory Generation—0
FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition—0
A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models—0
Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review—0
Distributed Agent-Based Collaborative Learning in Cross-Individual Wearable Sensor-Based Human Activity Recognition—0
rTsfNet: a DNN model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition—0
On the recognition of the game type based on physiological signals and eye tracking—0
Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition—0
ConViViT -- A Deep Neural Network Combining Convolutions and Factorized Self-Attention for Human Activity Recognition—0
On the Benefit of Generative Foundation Models for Human Activity Recognition—0
Too Good To Be True: performance overestimation in (re)current practices for Human Activity Recognition—0
Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport—0
Antenna Response Consistency Driven Self-supervised Learning for WIFI-based Human Activity Recognition—0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—0
Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Structured Keypoint PoolingAccuracy93.4—Unverified
2Semi-Supervised Hard Attention (SSHA); pretrained on Deepmind Kinetics datasetAccuracy90.4—Unverified
3Human Skeletons + Change DetectionAccuracy90.25—Unverified
4Separable Convolutional LSTMAccuracy89.75—Unverified
5SPIL ConvolutionAccuracy89.3—Unverified
6Flow Gated NetworkAccuracy87.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FocusCLIPTop-3 Accuracy (%)10.47—Unverified
2CLIPTop-3 Accuracy (%)6.49—Unverified
#ModelMetricClaimedVerifiedStatus
1Boutaleb et al.1:1 Accuracy97.91—Unverified
#ModelMetricClaimedVerifiedStatus
1all-landmark-modelActivity Recognition0.76—Unverified