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 751–775 of 1322 papers

TitleStatusHype
A Feature Selection Method for Multi-Dimension Time-Series Data—0
Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis—0
Self-Supervised WiFi-Based Activity Recognition—0
Ego-Exo: Transferring Visual Representations from Third-person to First-person VideosCode1
Spatiotemporal Deformable Scene Graphs for Complex Activity Detection—0
Personalized Semi-Supervised Federated Learning for Human Activity Recognition—0
Description of Structural Biases and Associated Data in Sensor-Rich Environments—0
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition—0
Multi-GAT: A Graphical Attention-based Hierarchical Multimodal Representation Learning Approach for Human Activity Recognition—0
Selective Feature Compression for Efficient Activity Recognition Inference—0
Online Learning Probabilistic Event Calculus Theories in Answer Set ProgrammingCode0
Human Activity Analysis and Recognition from Smartphones using Machine Learning Techniques—0
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence—0
MIcro-Surgical Anastomose Workflow recognition challenge report—0
Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors—0
Unsupervised Doppler Radar-Based Activity Recognition for e-Healthcare—0
KU-HAR: An open dataset for heterogeneous human activity recognitionCode0
SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access PointsCode1
Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using SmartphonesCode1
BASAR:Black-box Attack on Skeletal Action RecognitionCode1
Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity RecognitionCode1
Efficient data-driven encoding of scene motion using Eccentricity—0
SimHumalator: An Open Source WiFi Based Passive Radar Human Simulator For Activity Recognition—0
Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers—0
Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data—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