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 101–125 of 1322 papers

TitleStatusHype
Temporally Consistent Dynamic Scene Graphs: An End-to-End Approach for Action Tracklet Generation—0
AsyMov: Integrated Sensing and Communications with Asynchronous Moving Devices—0
RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable DataCode1
Heterogeneous Relationships of Subjects and Shapelets for Semi-supervised Multivariate Series Classification—0
Adaptive Client Selection with Personalization for Communication Efficient Federated LearningCode0
Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams—0
Resolution-Adaptive Micro-Doppler Spectrogram for Human Activity RecognitionCode0
DATTA: Domain-Adversarial Test-Time Adaptation for Cross-Domain WiFi-Based Human Activity RecognitionCode1
Bi-LSTM neural network for EEG-based error detection in musicians' performance—0
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal TransportCode0
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems—0
Process-aware Human Activity Recognition—0
Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task—0
Federated Split Learning for Human Activity Recognition with Differential Privacy—0
Autoregressive Adaptive Hypergraph Transformer for Skeleton-based Activity RecognitionCode1
WiFlexFormer: Efficient WiFi-Based Person-Centric SensingCode0
Explaining Human Activity Recognition with SHAP: Validating Insights with Perturbation and Quantitative MeasuresCode0
ARN-LSTM: A Multi-Stream Fusion Model for Skeleton-based Action Recognition—0
Differentially Private Integrated Decision Gradients (IDG-DP) for Radar-based Human Activity RecognitionCode0
Approaches to human activity recognition via passive radarCode0
First Place Solution to the ECCV 2024 ROAD++ Challenge @ ROAD++ Atomic Activity Recognition 2024—0
LiGAR: LiDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition—0
Mukhtasir-Khail-Net: An Ultra-Efficient Convolutional Neural Network for Sports Activity Recognition with Wearable Inertial SensorsCode0
Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy—0
CKSP: Cross-species Knowledge Sharing and Preserving for Universal Animal 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