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 126–150 of 1322 papers

TitleStatusHype
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
Adaptation of Surgical Activity Recognition Models Across Operating Rooms—0
An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition—0
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition—0
An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data—0
AdaFPP: Adapt-Focused Bi-Propagating Prototype Learning for Panoramic Activity Recognition—0
A Critical Analysis on Machine Learning Techniques for Video-based Human Activity Recognition of Surveillance Systems: A Review—0
An Intelligent Non-Invasive Real Time Human Activity Recognition System for Next-Generation Healthcare—0
An Integrated Approach to Crowd Video Analysis: From Tracking to Multi-level Activity Recognition—0
Actor-Transformers for Group Activity Recognition—0
A Causality-Aware Pattern Mining Scheme for Group Activity Recognition in a Pervasive Sensor Space—0
Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals—0
An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos—0
A new network-based algorithm for human activity recognition in video—0
Differential Recurrent Neural Network and its Application for Human Activity Recognition—0
An Event Calculus Production Rule System for Reasoning in Dynamic and Uncertain Domains—0
A Neurorobotics Approach to Behaviour Selection based on Human Activity Recognition—0
Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging—0
Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors—0
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
An Efficient Data Imputation Technique for Human Activity Recognition—0
Activity recognition using ST-GCN with 3D motion data—0
Activity Recognition Using A Combination of Category Components And Local Models for Video Surveillance—0
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies—0
A compressive multi-kernel method for privacy-preserving machine learning—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