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 376–400 of 1322 papers

TitleStatusHype
Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition—0
Beyond Isolated Frames: Enhancing Sensor-Based Human Activity Recognition through Intra- and Inter-Frame Attention—0
Analysis of Gait Pattern to Recognize the Human Activities—0
Activity Recognition on a Large Scale in Short Videos - Moments in Time Dataset—0
Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets—0
Beyond Actions: Discriminative Models for Contextual Group Activities—0
An adaptable cognitive microcontroller node for fitness activity recognition—0
Benchmark of DNN Model Search at Deployment Time—0
Benchmarking Classical, Deep, and Generative Models for Human 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 Review of Automated Data Annotation Techniques in Human Activity Recognition—0
Benchmarking 2D Egocentric Hand Pose Datasets—0
An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson's Disease—0
Batch-Based Activity Recognition from Egocentric Photo-Streams—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
BAR: Bayesian Activity Recognition using variational inference—0
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization—0
A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition—0
Balancing Continual Learning and Fine-tuning for Human Activity Recognition—0
Background Knowledge Injection for Interpretable Sequence Classification—0
A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation—0
Activity Recognition From Newborn Resuscitation Videos—0
A Comprehensive Overview on UWB Radar: Applications, Standards, Signal Processing Techniques, Datasets, Radio Chips, Trends and Future Research Directions—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