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 951–975 of 1322 papers

TitleStatusHype
Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data—0
Template co-updating in multi-modal human activity recognition systems—0
RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition—0
Skeleton based Activity Recognition by Fusing Part-wise Spatio-temporal and Attention Driven Residues—0
Towards Hardware-Aware Tractable Learning of Probabilistic ModelsCode0
Topological Machine Learning for Multivariate Time SeriesCode0
Towards Fairness in Visual Recognition: Effective Strategies for Bias MitigationCode0
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features—0
Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach—0
Simultaneous Implementation Features Extraction and Recognition Using C3D Network for WiFi-based Human Activity Recognition—0
RWF-2000: An Open Large Scale Video Database for Violence DetectionCode0
Privacy and Utility Preserving Sensor-Data TransformationsCode1
Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning—0
Chirality Nets for Human Pose RegressionCode0
Model enhancement and personalization using weakly supervised learning for multi-modal mobile sensing—0
A systematic review of smartphone-based human activity recognition for health research—0
New Convex Relaxations for MRF Inference With Unknown Graphs—0
Toyota Smarthome: Real-World Activities of Daily Living—0
Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference—0
Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles—0
Generating Fair Universal Representations using Adversarial Models—0
Sign Language Recognition Analysis using Multimodal Data—0
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices—0
Simultaneous Segmentation and Recognition: Towards more accurate Ego Gesture Recognition—0
Meta-Learning for Few-Shot Time Series Classification—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