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 851–875 of 1322 papers

TitleStatusHype
Self-Supervised Human Activity Recognition by Augmenting Generative Adversarial Networks—0
Drive Safe: Cognitive-Behavioral Mining for Intelligent Transportation Cyber-Physical System—0
AssembleNet++: Assembling Modality Representations via Attention ConnectionsCode0
Skeleton-based Action Recognition via Spatial and Temporal Transformer NetworksCode1
An Intelligent Non-Invasive Real Time Human Activity Recognition System for Next-Generation Healthcare—0
DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor DataCode1
SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks—0
A Novel Indoor Positioning System for unprepared firefighting scenarios—0
HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm—0
SeCo: Exploring Sequence Supervision for Unsupervised Representation LearningCode1
Towards Deep Clustering of Human Activities from Wearables—0
Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition—0
Empowering Relational Network by Self-Attention Augmented Conditional Random Fields for Group Activity Recognition—0
AssembleNet++: Assembling Modality Representations via Attention Connections - Supplementary Material -—0
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised LearningCode1
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor dataCode1
Directional Temporal Modeling for Action Recognition—0
Creating a Large-scale Synthetic Dataset for Human Activity Recognition—0
Social Adaptive Module for Weakly-supervised Group Activity Recognition—0
Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors—0
Spectrum-Guided Adversarial Disparity LearningCode0
Fusing Motion Patterns and Key Visual Information for Semantic Event Recognition in Basketball Videos—0
Knowledge Graph Driven Approach to Represent Video Streams for Spatiotemporal Event Pattern Matching in Complex Event Processing—0
Transfer Learning for Activity Recognition in Mobile HealthCode0
An Efficient Data Imputation Technique for Human Activity Recognition—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