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 201–225 of 1322 papers

TitleStatusHype
VCHAR:Variance-Driven Complex Human Activity Recognition framework with Generative Representation—0
Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing DataCode0
Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge TransferCode0
Towards LLM-Powered Ambient Sensor Based Multi-Person Human Activity Recognition—0
Feature Fusion for Human Activity Recognition using Parameter-Optimized Multi-Stage Graph Convolutional Network and Transformer Models—0
Leveraging LDA Feature Extraction to Augment Human Activity Recognition AccuracyCode0
Self-supervised Multi-actor Social Activity Understanding in Streaming Videos—0
Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homes—0
Game of LLMs: Discovering Structural Constructs in Activities using Large Language Models—0
EarDA: Towards Accurate and Data-Efficient Earable Activity Sensing—0
Unsupervised explainable activity prediction in competitive Nordic Walking from experimental data—0
Initial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognition—0
GPT-4o: Visual perception performance of multimodal large language models in piglet activity understanding—0
Enhancing Activity Recognition After Stroke: Generative Adversarial Networks for Kinematic Data Augmentation—0
Video-based Exercise Classification and Activated Muscle Group Prediction with Hybrid X3D-SlowFast Network—0
Large Language Models Memorize Sensor Datasets! Implications on Human Activity Recognition Research—0
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition—0
MuJo: Multimodal Joint Feature Space Learning for Human Activity Recognition—0
FLOW: Fusing and Shuffling Global and Local Views for Cross-User Human Activity Recognition with IMUs—0
iKAN: Global Incremental Learning with KAN for Human Activity Recognition Across Heterogeneous Datasets—0
HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model—0
Estimating Human Poses Across Datasets: A Unified Skeleton and Multi-Teacher Distillation Approach—0
Flow-Assisted Motion Learning Network for Weakly-Supervised Group Activity Recognition—0
Wearable-based behaviour interpolation for semi-supervised human activity recognition—0
Beyond Isolated Frames: Enhancing Sensor-Based Human Activity Recognition through Intra- and Inter-Frame Attention—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