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 326–350 of 1322 papers

TitleStatusHype
Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series—0
From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safety—0
Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition—0
A Causality-Aware Pattern Mining Scheme for Group Activity Recognition in a Pervasive Sensor Space—0
Student Activity Recognition in Classroom Environments using Transfer Learning—0
Action-slot: Visual Action-centric Representations for Multi-label Atomic Activity Recognition in Traffic ScenesCode1
REACT: Recognize Every Action Everywhere All At Once—0
Temporal Action Localization for Inertial-based Human Activity RecognitionCode1
Contrastive Left-Right Wearable Sensors (IMUs) Consistency Matching for HAR—0
Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes—0
Passive Human Sensing Enhanced by Reconfigurable Intelligent Surface: Opportunities and Challenges—0
MVSA-Net: Multi-View State-Action Recognition for Robust and Deployable Trajectory Generation—0
TENT: Connect Language Models with IoT Sensors for Zero-Shot Activity Recognition—0
FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition—0
A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models—0
Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained EnvironmentsCode1
Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review—0
Distributed Agent-Based Collaborative Learning in Cross-Individual Wearable Sensor-Based Human Activity Recognition—0
rTsfNet: a DNN model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition—0
Optimization-Free Test-Time Adaptation for Cross-Person Activity RecognitionCode1
On the recognition of the game type based on physiological signals and eye tracking—0
Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition—0
ConViViT -- A Deep Neural Network Combining Convolutions and Factorized Self-Attention for Human Activity Recognition—0
Too Good To Be True: performance overestimation in (re)current practices for Human Activity Recognition—0
On the Benefit of Generative Foundation Models 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