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 76–100 of 1322 papers

TitleStatusHype
FlowAR: une plateforme uniformisée pour la reconnaissance des activités humaines à partir de capteurs binaires—0
ESPARGOS: An Ultra Low-Cost, Realtime-Capable Multi-Antenna WiFi Channel Sounder—0
Low-power Spike-based Wearable Analytics on RRAM Crossbars—0
SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human ActivityCode0
CNN Autoencoders for Hierarchical Feature Extraction and Fusion in Multi-sensor Human Activity Recognition—0
Scaling laws in wearable human activity recognition—0
Assessing the Impact of Sampling Irregularity in Time Series Data: Human Activity Recognition As A Case Study—0
Human Activity Recognition with a 6.5 GHz Reconfigurable Intelligent Surface for Wi-Fi 6E—0
Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity RecognitionCode0
Benchmarking Classical, Deep, and Generative Models for Human Activity Recognition—0
Initial Findings on Sensor based Open Vocabulary Activity Recognition via Text Embedding Inversion—0
IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for HealthcareCode0
Collaborative Human Activity Recognition with Passive Inter-Body Electrostatic Field—0
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations—0
Multivariate Human Activity Segmentation: Systematic Benchmark with ClaSPCode0
OV-HHIR: Open Vocabulary Human Interaction Recognition Using Cross-modal Integration of Large Language Models—0
Transformer-Based Contrastive Meta-Learning For Low-Resource Generalizable Activity Recognition—0
Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity RecognitionCode0
Hierarchical Temporal Convolution Network:Towards Privacy-Centric Activity RecognitionCode0
Choose Your Explanation: A Comparison of SHAP and GradCAM in Human Activity RecognitionCode0
Generalizable Sensor-Based Activity Recognition via Categorical Concept Invariant Learning—0
Wearable Accelerometer Foundation Models for Health via Knowledge Distillation—0
Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets—0
Exploring the Impact of Synthetic Data on Human Gesture Recognition Tasks Using GANs—0
Action Recognition based Industrial Safety Violation DetectionCode0
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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