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 751–775 of 1322 papers

TitleStatusHype
Question Type Guided Attention in Visual Question Answering—0
randomHAR: Improving Ensemble Deep Learners for Human Activity Recognition with Sensor Selection and Reinforcement Learning—0
Random Projections and Natural Sparsity in Time-Series Classification: A Theoretical Analysis—0
Ranking the Top-K Realizations of Stochastically Known Event Logs—0
RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing—0
Rate-Invariant Analysis of Trajectories on Riemannian Manifolds with Application in Visual Speech Recognition—0
Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection—0
REACT: Recognize Every Action Everywhere All At Once—0
Real-Time Activity Recognition and Intention Recognition Using a Vision-based Embedded System—0
Real-time Aerial Detection and Reasoning on Embedded-UAVs—0
Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices—0
Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning—0
RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonics—0
Recognition and Prediction of Surgical Gestures and Trajectories Using Transformer Models in Robot-Assisted Surgery—0
Recognition of Physiological Patterns during Activities of Daily Living Using Wearable Biosignal Sensors—0
Recognize Human Activities from Partially Observed Videos—0
Recognizing Activities of Daily Living from Egocentric Images—0
Recognizing Activities via Bag of Words for Attribute Dynamics—0
Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data—0
Recognizing Plans by Learning Embeddings from Observed Action Distributions—0
Recurrent Modeling of Interaction Context for Collective Activity Recognition—0
Redundant feature screening method for human activity recognition based on attention purification mechanism—0
ReHAR: Robust and Efficient Human Activity Recognition—0
Relevance Topic Model for Unstructured Social Group Activity Recognition—0
Reshaping Visual Datasets for Domain Adaptation—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