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 451–475 of 1322 papers

TitleStatusHype
DNN Transfer Learning from Diversified Micro-Doppler for Motion Classification—0
Enabling Edge Cloud Intelligence for Activity Learning in Smart Home—0
Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders—0
Encoding Based Saliency Detection for Videos and Images—0
Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone—0
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations—0
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification—0
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection—0
Enhancing Smart Environments with Context-Aware Chatbots using Large Language Models—0
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization—0
EnHDC: Ensemble Learning for Brain-Inspired Hyperdimensional Computing—0
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition—0
WearableMil: An End-to-End Framework for Military Activity Recognition and Performance Monitoring—0
Entropy Decision Fusion for Smartphone Sensor based Human Activity Recognition—0
Activity Recognition based on a Magnitude-Orientation Stream Network—0
ESPARGOS: An Ultra Low-Cost, Realtime-Capable Multi-Antenna WiFi Channel Sounder—0
A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities—0
Estimating Human Poses Across Datasets: A Unified Skeleton and Multi-Teacher Distillation Approach—0
Distribution estimation and change-point estimation for time series via DNN-based GANs—0
Evaluating Deep Neural Network Ensembles by Majority Voting cum Meta-Learning scheme—0
Automated Surgical Activity Recognition with One Labeled Sequence—0
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing—0
Evaluation of Encoding Schemes on Ubiquitous Sensor Signal for Spiking Neural Network—0
Evaluation of Regularization-based Continual Learning Approaches: Application to HAR—0
Automated Level Crossing System: A Computer Vision Based Approach with Raspberry Pi Microcontroller—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