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 551–575 of 1322 papers

TitleStatusHype
VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild—0
EdgeServe: A Streaming System for Decentralized Model Serving—0
Knowledge Augmented Relation Inference for Group Activity Recognition—0
Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism—0
A Preliminary Study on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data—0
FG-SSA: Features Gradient-based Signals Selection Algorithm of Linear Complexity for Convolutional Neural Networks—0
Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition—0
On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge—0
cGAN-Based High Dimensional IMU Sensor Data Generation for Enhanced Human Activity Recognition in Therapeutic Activities—0
Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration ApplicationsCode0
InMyFace: Inertial and Mechanomyography-Based Sensor Fusion for Wearable Facial Activity Recognition—0
PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation—0
Ensemble Learning for Fusion of Multiview Vision with Occlusion and Missing Information: Framework and Evaluations with Real-World Data and Applications in Driver Hand Activity Recognition—0
Optical Flow Estimation in 360^ Videos: Dataset, Model and Application—0
Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition—0
Dataset Bias in Human Activity Recognition—0
Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data—0
Your Day in Your Pocket: Complex Activity Recognition from Smartphone Accelerometers—0
Exploring Automatic Gym Workouts Recognition Locally On Wearable Resource-Constrained Devices—0
EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living—0
A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data—0
SkeleTR: Towards Skeleton-based Action Recognition in the Wild—0
An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity—0
Unleashing the Power of Shared Label Structures for Human Activity Recognition—0
Cross-modal Scalable Hierarchical Clustering in Hyperbolic space—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