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 976–1000 of 1322 papers

TitleStatusHype
Activity recognition using ST-GCN with 3D motion data—0
Can a simple approach identify complex nurse care activity?—0
Nurse care activity recognition challenge: summary and results—0
Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer—0
Personalizing Smartwatch Based Activity Recognition Using Transfer Learning—0
Temporal Reasoning Graph for Activity Recognition—0
Human activity recognition from skeleton posesCode0
Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing—0
Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback—0
Wi-Fringe: Leveraging Text Semantics in WiFi CSI-Based Device-Free Named Gesture Recognition—0
MEx: Multi-modal Exercises Dataset for Human Activity RecognitionCode0
Three Branches: Detecting Actions With Richer Features—0
Progressive Relation Learning for Group Activity Recognition—0
Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity RecognitionCode0
Mindful Active LearningCode0
Multi-task Self-Supervised Learning for Human Activity Detection—0
Multiple Human Association between Top and Horizontal Views by Matching Subjects' Spatial Distributions—0
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare—0
Automated Surgical Activity Recognition with One Labeled Sequence—0
Joint Activity Recognition and Indoor Localization With WiFi FingerprintsCode0
An end-to-end (deep) neural network applied to raw EEG, fNIRs and body motion data for data fusion and BCI classification task without any pre-/post-processing—0
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence ModelCode0
AVD: Adversarial Video Distillation—0
Tweets Can Tell: Activity Recognition using Hybrid Long Short-Term Memory Model—0
Image based Eye Gaze Tracking and its Applications—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