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 1201–1225 of 1322 papers

TitleStatusHype
Human Activity Recognition using Continuous Wavelet Transform and Convolutional Neural NetworksCode0
SBGAR: Semantics Based Group Activity RecognitionCode0
PI-Net: A Deep Learning Approach to Extract Topological Persistence ImagesCode0
M-PACT: An Open Source Platform for Repeatable Activity Classification ResearchCode0
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity RecognitionCode0
Pooled Motion Features for First-Person VideosCode0
Mukhtasir-Khail-Net: An Ultra-Efficient Convolutional Neural Network for Sports Activity Recognition with Wearable Inertial SensorsCode0
Human Activity Recognition using Multi-Head CNN followed by LSTMCode0
TxP: Reciprocal Generation of Ground Pressure Dynamics and Activity Descriptions for Improving Human Activity RecognitionCode0
Enhancing Wearable Tap Water Audio Detection through Subclass Annotation in the HD-Epic DatasetCode0
Pose And Joint-Aware Action RecognitionCode0
Robust Explainer Recommendation for Time Series ClassificationCode0
AdaRNN: Adaptive Learning and Forecasting of Time SeriesCode0
Ubicoustics: Plug-and-Play Acoustic Activity RecognitionCode0
A Survey of Human Activity Recognition Using WiFi CSICode0
Enhanced Spatio- Temporal Image Encoding for Online Human Activity RecognitionCode0
DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity RecognitionCode0
Human Activity Recognition with Convolutional Neural NetowrksCode0
PoseTrack: A Benchmark for Human Pose Estimation and TrackingCode0
Attention-Refined Unrolling for Sparse Sequential micro-Doppler ReconstructionCode0
Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V ProcessorsCode0
Adaptive Client Selection with Personalization for Communication Efficient Federated LearningCode0
A benchmark of data stream classification for human activity recognition on connected objectsCode0
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence ModelCode0
SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human ActivityCode0
Show:102550
← PrevPage 49 of 53Next →
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