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 1101–1125 of 1322 papers

TitleStatusHype
Reshaping Visual Datasets for Domain Adaptation—0
Re-Sign: Re-Aligned End-To-End Sequence Modelling With Deep Recurrent CNN-HMMs—0
Resource-Efficient Computing in Wearable Systems—0
Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification—0
Resource-Eficient Continual Learning for Sensor-Based Human Activity Recognition—0
Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging—0
RISAR: RIS-assisted Human Activity Recognition with Commercial Wi-Fi Devices—0
Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning—0
Robust Automated Human Activity Recognition and its Application to Sleep Research—0
Robust Multimodal Fusion for Human Activity Recognition—0
SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition—0
Robust Trajectory-based Density Estimation for Geometric Structure Recovery: Theory and Applications—0
RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition—0
rTsfNet: a DNN model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition—0
rWISDM: Repaired WISDM, a Public Dataset for Human Activity Recognition—0
ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUs—0
Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques—0
Scaling laws in wearable human activity recognition—0
Scaling Wearable Foundation Models—0
Generalized Relevance Learning Grassmann QuantizationCode0
Directional Antenna Systems for Long-Range Through-Wall Human Activity RecognitionCode0
Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart HomesCode0
Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous DataCode0
Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity RecognitionCode0
Leveraging LDA Feature Extraction to Augment Human Activity Recognition AccuracyCode0
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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