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 801–850 of 1322 papers

TitleStatusHype
Uncertainty-sensitive Activity Recognition: a Reliability Benchmark and the CARING Models—0
Context Aware Group Activity Recognition—0
Anonymizing Egocentric Videos—0
Anomaly detection and regime searching in fitness-tracker data—0
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition—0
Deep Positive Unlabeled Learning with a Sequential Bias—0
Sequence Metric Learning as Synchronization of Recurrent Neural Networks—0
SkipW: Resource adaptable RNN with strict upper computational limit—0
3D Human motion anticipation and classification—0
Invariant Feature Learning for Sensor-based Human Activity Recognition—0
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring—0
Interactive Fusion of Multi-level Features for Compositional Activity RecognitionCode1
T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis—0
Contrastive Predictive Coding for Human Activity Recognition—0
Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks—0
Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring—0
MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection—0
Fine-grained activity recognition for assembly videos—0
Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart HomesCode0
A compact sequence encoding scheme for online human activity recognition in HRI applications—0
Post-training Iterative Hierarchical Data Augmentation for Deep Networks—0
Exploring Contrastive Learning in Human Activity Recognition for HealthcareCode1
LaHAR: Latent Human Activity Recognition using LDACode0
Yet it moves: Learning from Generic Motions to Generate IMU data from YouTube videos—0
Self-Supervised Transformers for Activity Classification using Ambient Sensors—0
Self-supervised transfer learning of physiological representations from free-living wearable dataCode1
Human activity recognition using improved dynamic image—0
Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition—0
Skeleton-based Relational Reasoning for Group Activity Analysis—0
Learning Generalizable Physiological Representations from Large-scale Wearable DataCode1
A Tree-structure Convolutional Neural Network for Temporal Features Exaction on Sensor-based Multi-resident Activity Recognition—0
Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous DataCode0
Semi-supervised Federated Learning for Activity Recognition—0
A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition—0
Bubblenet: A Disperse Recurrent Structure To Recognize Activities—0
HHAR-net: Hierarchical Human Activity Recognition using Neural NetworksCode1
Improved Actor Relation Graph based Group Activity RecognitionCode1
Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion—0
Pose And Joint-Aware Action RecognitionCode0
Egok360: A 360 Egocentric Kinetic Human Activity Video Dataset—0
Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network—0
Deep learning for time series classificationCode2
Attention-Driven Body Pose Encoding for Human Activity Recognition—0
Semi-supervised sequence classification through change point detection—0
Stacked Generalization for Human Activity Recognition—0
MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model—0
Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations—0
Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone—0
Personalization in Human Activity Recognition—0
A benchmark of data stream classification for human activity recognition on connected objectsCode0
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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