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 1026–1050 of 1322 papers

TitleStatusHype
Activity Modeling in Smart Home using High Utility Pattern Mining over Data Streams—0
Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning—0
ActivityNet Challenge 2017 Summary—0
Activity Recognition and Prediction in Real Homes—0
Activity Recognition based on a Magnitude-Orientation Stream Network—0
Activity Recognition From Newborn Resuscitation Videos—0
Activity recognition from videos with parallel hypergraph matching on GPUs—0
Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs—0
Activity Recognition on a Large Scale in Short Videos - Moments in Time Dataset—0
Activity Recognition Using A Combination of Category Components And Local Models for Video Surveillance—0
Activity recognition using ST-GCN with 3D motion data—0
Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging—0
Actor-Transformers for Group Activity Recognition—0
AdaFPP: Adapt-Focused Bi-Propagating Prototype Learning for Panoramic Activity Recognition—0
Adaptation of Surgical Activity Recognition Models Across Operating Rooms—0
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
Adaptive Feature Processing for Robust Human Activity Recognition on a Novel Multi-Modal Dataset—0
AdaSense: Adaptive Low-Power Sensing and Activity Recognition for Wearable Devices—0
A dataset for complex activity recognition withmicro and macro activities in a cooking scenario—0
A Deep Learning Approach To Multiple Kernel Fusion—0
A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring—0
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors—0
A Deep Structured Model with Radius-Margin Bound for 3D Human Activity Recognition—0
A distillation-based approach integrating continual learning and federated learning for pervasive services—0
Advancing Location-Invariant and Device-Agnostic Motion Activity Recognition on Wearable Devices—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