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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 676–700 of 1322 papers

TitleStatusHype
Nurse care activity recognition challenge: summary and results—0
Object and Text-guided Semantics for CNN-based Activity Recognition—0
Octave Mix: Data augmentation using frequency decomposition for activity recognition—0
On Attention Models for Human Activity Recognition—0
On Flow Profile Image for Video Representation—0
On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge—0
Online Collective Animal Movement Activity Recognition—0
To Store or Not? Online Data Selection for Federated Learning with Limited Storage—0
Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback—0
Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning—0
Online Human Activity Recognition Employing Hierarchical Hidden Markov Models—0
Online Human Activity Recognition using Low-Power Wearable Devices—0
On Matched Filtering for Statistical Change Point Detection—0
On Multi-resident Activity Recognition in Ambient Smart-Homes—0
On Neural Inertial Classification Networks for Pedestrian Activity Recognition—0
On the Benefit of Generative Foundation Models for Human Activity Recognition—0
On the recognition of the game type based on physiological signals and eye tracking—0
On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams—0
OpenPack: A Large-scale Dataset for Recognizing Packaging Works in IoT-enabled Logistic Environments—0
Optical Flow Estimation in 360^ Videos: Dataset, Model and Application—0
Optimised Convolutional Neural Networks for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications—0
Optimized Gated Deep Learning Architectures for Sensor Fusion—0
Ordered Atomic Activity for Fine-grained Interactive Traffic Scenario Understanding—0
Order Matters: On Parameter-Efficient Image-to-Video Probing for Recognizing Nearly Symmetric Actions—0
Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models—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