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 1251–1275 of 1322 papers

TitleStatusHype
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN FeaturesCode0
WSense: A Robust Feature Learning Module for Lightweight Human Activity RecognitionCode0
The Contribution of Human Body Capacitance/Body-Area Electric Field To Individual and Collaborative Activity RecognitionCode0
Through-the-Wall Radar Human Activity Recognition WITHOUT Using Neural NetworksCode0
Multivariate Human Activity Segmentation: Systematic Benchmark with ClaSPCode0
Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity RecognitionCode0
Approaches to human activity recognition via passive radarCode0
Multivariate Time Series Classification using Dilated Convolutional Neural NetworkCode0
Weakly-guided Self-supervised Pretraining for Temporal Activity DetectionCode0
B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflowsCode0
Multi-view Video-Pose Pretraining for Operating Room Surgical Activity RecognitionCode0
Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor DiversityCode0
Domain Adaptation with Representation Learning and Nonlinear Relation for Time SeriesCode0
A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization with Gaussian MutationCode0
Interpretable 3D Human Action Analysis with Temporal Convolutional NetworksCode0
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity RecognitionCode0
Alignment-based conformance checking over probabilistic eventsCode0
Cross-modal Knowledge Distillation for Vision-to-Sensor Action RecognitionCode0
Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment RecognitionCode0
Investigating Enhancements to Contrastive Predictive Coding for Human Activity RecognitionCode0
MyDigitalFootprint: an extensive context dataset for pervasive computing applications at the edgeCode0
IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for HealthcareCode0
MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained DevicesCode0
ConvBoost: Boosting ConvNets for Sensor-based Activity RecognitionCode0
Joint Activity Recognition and Indoor Localization With WiFi FingerprintsCode0
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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