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 501–525 of 1322 papers

TitleStatusHype
Feature Fusion for Human Activity Recognition using Parameter-Optimized Multi-Stage Graph Convolutional Network and Transformer Models—0
Feature Learning for Interaction Activity Recognition in RGBD Videos—0
Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition—0
Distributed Agent-Based Collaborative Learning in Cross-Individual Wearable Sensor-Based Human Activity Recognition—0
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition—0
Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain—0
Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network—0
Federated Multi-task Hierarchical Attention Model for Sensor Analytics—0
Disparity-Augmented Trajectories for Human Activity Recognition—0
Federated Split Learning for Human Activity Recognition with Differential Privacy—0
Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data—0
Automated Activity Recognition of Construction Equipment Using a Data Fusion Approach—0
Activity Recognition and Prediction in Real Homes—0
Discriminative training for Convolved Multiple-Output Gaussian processes—0
Discriminative Hierarchical Rank Pooling for Activity Recognition—0
Automated Activity Recognition in Clinical Documents—0
Discriminating sensor activation in activity recognition within multi-occupancy environments based on nearby interaction—0
Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition—0
DISC: a Dataset for Integrated Sensing and Communication in mmWave Systems—0
Directional Temporal Modeling for Action Recognition—0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—0
A Masked Semi-Supervised Learning Approach for Otago Micro Labels Recognition—0
ActivityNet Challenge 2017 Summary—0
3D Human motion anticipation and classification—0
Model enhancement and personalization using weakly supervised learning for multi-modal mobile sensing—0
Show:102550
← PrevPage 21 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