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 426–450 of 1322 papers

TitleStatusHype
Domain-Adversarial Anatomical Graph Networks for Cross-User Human Activity Recognition—0
AVD: Adversarial Video Distillation—0
EarDA: Towards Accurate and Data-Efficient Earable Activity Sensing—0
Early Improving Recurrent Elastic Highway Network—0
Early Mobility Recognition for Intensive Care Unit Patients Using Accelerometers—0
Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors—0
Eco-Friendly Sensing for Human Activity Recognition—0
EdgeServe: A Streaming System for Decentralized Model Serving—0
Effective Human Activity Recognition Based on Small Datasets—0
Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors—0
Efficient data-driven encoding of scene motion using Eccentricity—0
EfficientFi: Towards Large-Scale Lightweight WiFi Sensing via CSI Compression—0
Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition—0
Efficient Retail Video Annotation: A Robust Key Frame Generation Approach for Product and Customer Interaction Analysis—0
Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey—0
Egocentric Activity Recognition and Localization on a 3D Map—0
Egocentric Activity Recognition on a Budget—0
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery—0
"Filling the Blanks'': Identifying Micro-activities that Compose Complex Human Activities of Daily Living—0
DNN Transfer Learning from Diversified Micro-Doppler for Motion Classification—0
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification—0
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection—0
Embedding Symbolic Temporal Knowledge into Deep Sequential Models—0
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization—0
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in 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