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 951–975 of 1322 papers

TitleStatusHype
Deep Learning for Inertial Sensor Alignment—0
Mouse Movement and Probabilistic Graphical Models Based E-Learning Activity Recognition Improvement Possibilistic Model—0
MPT-PAR:Mix-Parameters Transformer for Panoramic Activity Recognition—0
MuJo: Multimodal Joint Feature Space Learning for Human Activity Recognition—0
Multi-agent Attentional Activity Recognition—0
Multi-channel Time Series Decomposition Network For Generalizable Sensor-Based Activity Recognition—0
Multi-Channel Time-Series Person and Soft-Biometric Identification—0
MultiCore+TPU Accelerated Multi-Modal TinyML for Livestock Behaviour Recognition—0
Multidimensional Human Activity Recognition With Large Language Model: A Conceptual Framework—0
Multi-GAT: A Graphical Attention-based Hierarchical Multimodal Representation Learning Approach for Human Activity Recognition—0
Multi-kernel learning of deep convolutional features for action recognition—0
Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations—0
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks—0
Multi-label Prediction in Time Series Data using Deep Neural Networks—0
Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition—0
Multi-Level Sequence GAN for Group Activity Recognition—0
Multimodal Contrastive Learning with Hard Negative Sampling for Human Activity Recognition—0
Multi-modal Egocentric Activity Recognition using Audio-Visual Features—0
Multi-Modal Gesture Recognition from Video and Surgical Tool Pose Information via Motion Invariants—0
Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification—0
Multi-Modal Unsupervised Pre-Training for Surgical Operating Room Workflow Analysis—0
Multi-objective Feature Selection in Remote Health Monitoring Applications—0
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis—0
Multiple Human Association between Top and Horizontal Views by Matching Subjects' Spatial Distributions—0
Multiple object tracking with context awareness—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