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 451–500 of 1322 papers

TitleStatusHype
Empowering Relational Network by Self-Attention Augmented Conditional Random Fields for Group Activity Recognition—0
Enabling Edge Cloud Intelligence for Activity Learning in Smart Home—0
Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders—0
Encoding Based Saliency Detection for Videos and Images—0
Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone—0
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations—0
Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey—0
An Efficient Data Imputation Technique for Human Activity Recognition—0
Enhancing Smart Environments with Context-Aware Chatbots using Large Language Models—0
BON: An extended public domain dataset for human activity recognition—0
Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images—0
Bonn Activity Maps: Dataset Description—0
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables—0
Entropy Decision Fusion for Smartphone Sensor based Human Activity Recognition—0
Convolutional Relational Machine for Group Activity Recognition—0
ESPARGOS: An Ultra Low-Cost, Realtime-Capable Multi-Antenna WiFi Channel Sounder—0
A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition—0
Estimating Human Poses Across Datasets: A Unified Skeleton and Multi-Teacher Distillation Approach—0
ConViViT -- A Deep Neural Network Combining Convolutions and Factorized Self-Attention for Human Activity Recognition—0
Evaluating Deep Neural Network Ensembles by Majority Voting cum Meta-Learning scheme—0
BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition—0
Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones—0
Evaluation of Encoding Schemes on Ubiquitous Sensor Signal for Spiking Neural Network—0
Evaluation of Regularization-based Continual Learning Approaches: Application to HAR—0
Event and Activity Recognition in Video Surveillance for Cyber-Physical Systems—0
Event-LSTM: An Unsupervised and Asynchronous Learning-based Representation for Event-based Data—0
EventSleep: Sleep Activity Recognition with Event Cameras—0
Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams—0
Expanding Frozen Vision-Language Models without Retraining: Towards Improved Robot Perception—0
Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition—0
Are Accelerometers for Activity Recognition a Dead-end?—0
Explainable Artificial Intelligence for Quantifying Interfering and High-Risk Behaviors in Autism Spectrum Disorder in a Real-World Classroom Environment Using Privacy-Preserving Video Analysis—0
Explainable Deep Learning Framework for Human Activity Recognition—0
Explaining, Analyzing, and Probing Representations of Self-Supervised Learning Models for Sensor-based Human Activity Recognition—0
Can a simple approach identify complex nurse care activity?—0
Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models—0
Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment—0
Exploring Automatic Gym Workouts Recognition Locally On Wearable Resource-Constrained Devices—0
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare—0
Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study—0
Exploring the Capabilities of LLMs for IMU-based Fine-grained Human Activity Understanding—0
Exploring the Impact of Synthetic Data on Human Gesture Recognition Tasks Using GANs—0
Exploring Transformers for On-Line Handwritten Signature Verification—0
CERN: Confidence-Energy Recurrent Network for Group Activity Recognition—0
Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding Learning—0
Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer—0
Contrastive Predictive Coding for Human Activity Recognition—0
Fast Low-parameter Video Activity Localization in Collaborative Learning Environments—0
Contrastive Left-Right Wearable Sensors (IMUs) Consistency Matching for HAR—0
ARC-Net: Activity Recognition Through Capsules—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