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 1151–1200 of 1322 papers

TitleStatusHype
A Correlation Based Feature Representation for First-Person Activity RecognitionCode0
Latent hypernet: Exploring all Layers from Convolutional Neural Networks—0
Simultaneous Joint and Object Trajectory Templates for Human Activity Recognition from 3-D Data—0
An Integrated Approach to Crowd Video Analysis: From Tracking to Multi-level Activity Recognition—0
PoseTrack: A Benchmark for Human Pose Estimation and TrackingCode0
ActivityNet Challenge 2017 Summary—0
Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data AnalysisCode0
Batch-based Activity Recognition from Egocentric Photo-Streams RevisitedCode0
SBGAR: Semantics Based Group Activity RecognitionCode0
Unsupervised Segmentation of Action Segments in Egocentric Videos using Gaze—0
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis—0
Latent Embeddings for Collective Activity Recognition—0
Human Activity Recognition Using Robust Adaptive Privileged Probabilistic Learning—0
Kernel Cross-CorrelatorCode0
CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations—0
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks—0
Action Classification and Highlighting in Videos—0
Batch-Based Activity Recognition from Egocentric Photo-Streams—0
A Survey of Human Activity Recognition Using WiFi CSICode0
Activity Recognition based on a Magnitude-Orientation Stream Network—0
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable SensorsCode0
Early Improving Recurrent Elastic Highway Network—0
Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding Learning—0
Zero-Shot Activity Recognition with Verb Attribute InductionCode0
Multi-kernel learning of deep convolutional features for action recognition—0
Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems—0
Deep Learning for Sensor-based Activity Recognition: A SurveyCode0
An Interactive Greedy Approach to Group Sparsity in High DimensionsCode0
Application of Transfer Learning Approaches in Multimodal Wearable Human Activity Recognition—0
Structure Optimization for Deep Multimodal Fusion Networks using Graph-Induced Kernels—0
Recurrent Modeling of Interaction Context for Collective Activity Recognition—0
Re-Sign: Re-Aligned End-To-End Sequence Modelling With Deep Recurrent CNN-HMMs—0
Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging—0
Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network—0
Hierarchical Attentive Recurrent TrackingCode0
Cross-Country Skiing Gears Classification using Deep Learning—0
MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPUCode0
Non-Uniform Subset Selection for Active Learning in Structured DataCode0
Discriminatively Learned Hierarchical Rank Pooling NetworksCode0
Distributed Online Learning of Event DefinitionsCode0
Identifying First-person Camera Wearers in Third-person Videos—0
A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring—0
Interpretable 3D Human Action Analysis with Temporal Convolutional NetworksCode0
Recognizing Activities of Daily Living from Egocentric Images—0
CERN: Confidence-Energy Recurrent Network for Group Activity Recognition—0
Generalized Rank Pooling for Activity Recognition—0
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity RecognitionCode0
Pose-conditioned Spatio-Temporal Attention for Human Action Recognition—0
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables—0
Progress Estimation and Phase Detection for Sequential Processes—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