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 201–225 of 1322 papers

TitleStatusHype
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery—0
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices—0
AI-Powered Non-Contact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing—0
A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition—0
A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models—0
A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition—0
Attention-Driven Body Pose Encoding for Human Activity Recognition—0
Balancing Continual Learning and Fine-tuning for Human Activity Recognition—0
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization—0
BAR: Bayesian Activity Recognition using variational inference—0
Activity Modeling in Smart Home using High Utility Pattern Mining over Data Streams—0
Batch-Based Activity Recognition from Egocentric Photo-Streams—0
An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson's Disease—0
Benchmarking 2D Egocentric Hand Pose Datasets—0
Benchmarking Classical, Deep, and Generative Models for Human Activity Recognition—0
Benchmark of DNN Model Search at Deployment Time—0
Beyond Actions: Discriminative Models for Contextual Group Activities—0
Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets—0
Beyond Isolated Frames: Enhancing Sensor-Based Human Activity Recognition through Intra- and Inter-Frame Attention—0
Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition—0
Activity Recognition on a Large Scale in Short Videos - Moments in Time Dataset—0
Bi-Causal: Group Activity Recognition via Bidirectional Causality—0
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies—0
Bilinear Programming for Human Activity Recognition with Unknown MRF Graphs—0
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering—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