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 476–500 of 1322 papers

TitleStatusHype
Event-LSTM: An Unsupervised and Asynchronous Learning-based Representation for Event-based Data—0
EventSleep: Sleep Activity Recognition with Event Cameras—0
Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams—0
Expanding Frozen Vision-Language Models without Retraining: Towards Improved Robot Perception—0
Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition—0
Are Accelerometers for Activity Recognition a Dead-end?—0
Explainable Artificial Intelligence for Quantifying Interfering and High-Risk Behaviors in Autism Spectrum Disorder in a Real-World Classroom Environment Using Privacy-Preserving Video Analysis—0
Explainable Deep Learning Framework for Human Activity Recognition—0
Explaining, Analyzing, and Probing Representations of Self-Supervised Learning Models for Sensor-based Human Activity Recognition—0
Can a simple approach identify complex nurse care activity?—0
Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models—0
Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment—0
Exploring Automatic Gym Workouts Recognition Locally On Wearable Resource-Constrained Devices—0
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare—0
Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study—0
Exploring the Capabilities of LLMs for IMU-based Fine-grained Human Activity Understanding—0
Exploring the Impact of Synthetic Data on Human Gesture Recognition Tasks Using GANs—0
Exploring Transformers for On-Line Handwritten Signature Verification—0
CERN: Confidence-Energy Recurrent Network for Group Activity Recognition—0
Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding Learning—0
Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer—0
Contrastive Predictive Coding for Human Activity Recognition—0
Fast Low-parameter Video Activity Localization in Collaborative Learning Environments—0
Contrastive Left-Right Wearable Sensors (IMUs) Consistency Matching for HAR—0
ARC-Net: Activity Recognition Through Capsules—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