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 26–50 of 1322 papers

TitleStatusHype
Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs—0
FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments—0
MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained DevicesCode0
SOS: A Shuffle Order Strategy for Data Augmentation in Industrial Human Activity Recognition—0
A Comparative Study of Human Activity Recognition: Motion, Tactile, and multi-modal Approaches—0
Activity and Subject Detection for UCI HAR Dataset with & without missing Sensor DataCode0
Domain-Adversarial Anatomical Graph Networks for Cross-User Human Activity Recognition—0
VaCDA: Variational Contrastive Alignment-based Scalable Human Activity Recognition—0
GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian ManifoldsCode0
TxP: Reciprocal Generation of Ground Pressure Dynamics and Activity Descriptions for Improving Human Activity RecognitionCode0
SignX: The Foundation Model for Sign Recognition—0
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization—0
RadMamba: Efficient Human Activity Recognition through Radar-based Micro-Doppler-Oriented Mamba State-Space ModelCode1
Exploring Video-Based Driver Activity Recognition under Noisy LabelsCode0
Differentially Private 2D Human Pose Estimation—0
MultiCore+TPU Accelerated Multi-Modal TinyML for Livestock Behaviour Recognition—0
Semantically Encoding Activity Labels for Context-Aware Human Activity RecognitionCode0
Human Activity Recognition using RGB-Event based Sensors: A Multi-modal Heat Conduction Model and A Benchmark DatasetCode2
Multi-Head Adaptive Graph Convolution Network for Sparse Point Cloud-Based Human Activity RecognitionCode1
Exploring the Capabilities of LLMs for IMU-based Fine-grained Human Activity Understanding—0
LSC-ADL: An Activity of Daily Living (ADL)-Annotated Lifelog Dataset Generated via Semi-Automatic Clustering—0
Order Matters: On Parameter-Efficient Image-to-Video Probing for Recognizing Nearly Symmetric Actions—0
Redundant feature screening method for human activity recognition based on attention purification mechanism—0
Data-driven worker activity recognition and picking efficiency estimation in manual strawberry harvesting—0
CMD-HAR: Cross-Modal Disentanglement for Wearable Human Activity Recognition—0
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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