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
Online Human Activity Recognition using Low-Power Wearable Devices—0
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition—0
Deep Adaptive Temporal Pooling for Activity Recognition—0
The ActivityNet Large-Scale Activity Recognition Challenge 2018 Summary—0
Starting Movement Detection of Cyclists Using Smart Devices—0
Multi-Scale Supervised Network for Human Pose Estimation—0
Can You Spot the Semantic Predicate in this Video?—0
Attention is All We Need: Nailing Down Object-centric Attention for Egocentric Activity RecognitionCode0
DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition—0
Deep Transfer Learning for Cross-domain Activity Recognition—0
Human Activity Recognition in RGB-D Videos by Dynamic Images—0
A Survey of Knowledge Representation in Service Robotics—0
Who did What at Where and When: Simultaneous Multi-Person Tracking and Activity Recognition—0
Multi-modal Egocentric Activity Recognition using Audio-Visual Features—0
Human Activity Prediction in Smart Home Environments with LSTM Neural Networks—0
Cross-position Activity Recognition with Stratified Transfer Learning—0
On Multi-resident Activity Recognition in Ambient Smart-Homes—0
Object Level Visual Reasoning in VideosCode0
Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-ArtCode0
Qiniu Submission to ActivityNet Challenge 2018—0
Visually Guided Spatial Relation Extraction from Text—0
Egocentric Activity Recognition on a Budget—0
Surgical Activity Recognition in Robot-Assisted Radical Prostatectomy using Deep Learning—0
STS Classification with Dual-stream CNN—0
On Attention Models for Human Activity Recognition—0
Show:102550
← PrevPage 45 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