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 151–175 of 1322 papers

TitleStatusHype
A compressive multi-kernel method for privacy-preserving machine learning—0
A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models—0
Background Knowledge Injection for Interpretable Sequence Classification—0
BAR: Bayesian Activity Recognition using variational inference—0
Activity Recognition on a Large Scale in Short Videos - Moments in Time Dataset—0
Analysis of Gait Pattern to Recognize the Human Activities—0
A Comprehensive Review of Automated Data Annotation Techniques in Human Activity Recognition—0
An adaptable cognitive microcontroller node for fitness activity recognition—0
An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity—0
Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs—0
A Comprehensive Overview on UWB Radar: Applications, Standards, Signal Processing Techniques, Datasets, Radio Chips, Trends and Future Research Directions—0
An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson's Disease—0
A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation—0
Activity recognition from videos with parallel hypergraph matching on GPUs—0
A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition—0
A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation—0
Activity Recognition From Newborn Resuscitation Videos—0
AVD: Adversarial Video Distillation—0
A MIMO Radar-Based Metric Learning Approach for Activity Recognition—0
Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors—0
"Filling the Blanks'': Identifying Micro-activities that Compose Complex Human Activities of Daily Living—0
Activity Recognition based on a Magnitude-Orientation Stream Network—0
A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities—0
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery—0
Maximum Likelihood Speed Estimation of Moving Objects in Video Signals—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