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 1051–1075 of 1322 papers

TitleStatusHype
Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment—0
Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition—0
Adversarial Transferability in Wearable Sensor Systems—0
A Feature Selection Method for Multi-Dimension Time-Series Data—0
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition—0
A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection—0
A Framework For Identifying Group Behavior Of Wild Animals—0
A framework for mining process models from emails logs—0
AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments—0
AHAR: Adaptive CNN for Energy-efficient Human Activity Recognition in Low-power Edge Devices—0
A Heat-Map-based Algorithm for Recognizing Group Activities in Videos—0
A Hybrid Framework for Action Recognition in Low-Quality Video Sequences—0
AI-Powered Non-Contact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing—0
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices—0
A Logic Programming Approach to Activity Recognition—0
A Masked Semi-Supervised Learning Approach for Otago Micro Labels Recognition—0
Maximum Likelihood Speed Estimation of Moving Objects in Video Signals—0
"Filling the Blanks'': Identifying Micro-activities that Compose Complex Human Activities of Daily Living—0
Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors—0
A MIMO Radar-Based Metric Learning Approach for Activity Recognition—0
A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation—0
A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition—0
A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation—0
An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson's Disease—0
An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity—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