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 1201–1250 of 1322 papers

TitleStatusHype
CNN Autoencoders for Hierarchical Feature Extraction and Fusion in Multi-sensor Human Activity Recognition—0
Coarse Temporal Attention Network (CTA-Net) for Driver's Activity Recognition—0
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR—0
Collaborative Human Activity Recognition with Passive Inter-Body Electrostatic Field—0
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition—0
Combined Static and Motion Features for Deep-Networks Based Activity Recognition in Videos—0
Compact CNN for Indexing Egocentric Videos—0
Comparative Analysis of XGBoost and Minirocket Algortihms for Human Activity Recognition—0
Complex Activity Recognition using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video—0
Concurrent Activity Recognition with Multimodal CNN-LSTM Structure—0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
Contact-Free Multi-Target Tracking Using Distributed Massive MIMO-OFDM Communication System: Prototype and Analysis—0
Contactless Human Activity Recognition using Deep Learning with Flexible and Scalable Software Define Radio—0
Context Aware Active Learning of Activity Recognition Models—0
Context Aware Group Activity Recognition—0
Context-Aware Query Selection for Active Learning in Event Recognition—0
Context-driven Active and Incremental Activity Recognition—0
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models—0
Contextual Relationship-based Activity Segmentation on an Event Stream in the IoT Environment with Multi-user Activities—0
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions—0
Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization—0
Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis—0
Continuous Human Activity Recognition using a MIMO Radar for Transitional Motion Analysis—0
Contrastive Learning for Time Series on Dynamic Graphs—0
Contrastive Left-Right Wearable Sensors (IMUs) Consistency Matching for HAR—0
Contrastive Predictive Coding for Human Activity Recognition—0
ConViViT -- A Deep Neural Network Combining Convolutions and Factorized Self-Attention for Human Activity Recognition—0
Convolutional Relational Machine for Group Activity Recognition—0
CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition—0
Creating a Large-scale Synthetic Dataset for Human Activity Recognition—0
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator—0
Cross-Country Skiing Gears Classification using Deep Learning—0
Cross-domain Activity Recognition via Substructural Optimal Transport—0
Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition—0
Cross-modal Learning for Multi-modal Video Categorization—0
Cross-modal Scalable Hierarchical Clustering in Hyperbolic space—0
Cross-position Activity Recognition with Stratified Transfer Learning—0
Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks—0
Cross-user activity recognition using deep domain adaptation with temporal relation information—0
Cross-user activity recognition via temporal relation optimal transport—0
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community—0
CSI-Based Cross-Domain Activity Recognition via Zero-Shot Prototypical Networks—0
CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Network—0
DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors—0
Data Distribution Dynamics in Real-World WiFi-Based Patient Activity Monitoring for Home Healthcare—0
Data-driven worker activity recognition and picking efficiency estimation in manual strawberry harvesting—0
Dataiku's Solution to SPHERE's Activity Recognition Challenge—0
Dataset Bias in Human Activity Recognition—0
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight—0
Decoding Children's Social Behavior—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