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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–175 of 1322 papers

TitleStatusHype
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
Towards Child-Inclusive Clinical Video Understanding for Autism Spectrum Disorder—0
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
Multidimensional Human Activity Recognition With Large Language Model: A Conceptual Framework—0
A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities—0
Integrating Audio Narrations to Strengthen Domain Generalization in Multimodal First-Person Action Recognition—0
Language-centered Human Activity Recognition—0
Benchmarking 2D Egocentric Hand Pose Datasets—0
A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition—0
Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images—0
Unified Framework with Consistency across Modalities for Human Activity RecognitionCode0
TASAR: Transfer-based Attack on Skeletal Action RecognitionCode1
Robotic Vision and Multi-View Synergy: Action and activity recognition in assisted living scenariosCode0
A Critical Analysis on Machine Learning Techniques for Video-based Human Activity Recognition of Surveillance Systems: A Review—0
Integrating Features for Recognizing Human Activities through Optimized Parameters in Graph Convolutional Networks and Transformer Architectures—0
Towards Sustainable Personalized On-Device Human Activity Recognition with TinyML and Cloud-Enabled Auto Deployment—0
Towards Battery-Free Wireless Sensing via Radio-Frequency Energy Harvesting—0
TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing Lines—0
A Comparison of Deep Learning and Established Methods for Calf Behaviour MonitoringCode0
Generative AI based Secure Wireless Sensing for ISAC Networks—0
Limitations in Employing Natural Language Supervision for Sensor-Based Human Activity Recognition -- And Ways to Overcome Them—0
Explainable Deep Learning Framework for Human Activity Recognition—0
ALS-HAR: Harnessing Wearable Ambient Light Sensors to Enhance IMU-based Human Activity RecogntionCode3
Action Recognition for Privacy-Preserving Ambient Assisted LivingCode0
Weak-Annotation of HAR Datasets using Vision Foundation ModelsCode0
Show:102550
← PrevPage 7 of 53Next →
All datasetsRWF-2000Stanford40First-Person Hand Action BenchmarkSelf-stimulatory Behavior Dataset

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