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 1201–1225 of 1322 papers

TitleStatusHype
CNN Autoencoders for Hierarchical Feature Extraction and Fusion in Multi-sensor Human Activity Recognition—0
Coarse Temporal Attention Network (CTA-Net) for Driver's Activity Recognition—0
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR—0
Collaborative Human Activity Recognition with Passive Inter-Body Electrostatic Field—0
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition—0
Combined Static and Motion Features for Deep-Networks Based Activity Recognition in Videos—0
Compact CNN for Indexing Egocentric Videos—0
Comparative Analysis of XGBoost and Minirocket Algortihms for Human Activity Recognition—0
Complex Activity Recognition using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video—0
Concurrent Activity Recognition with Multimodal CNN-LSTM Structure—0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
Contact-Free Multi-Target Tracking Using Distributed Massive MIMO-OFDM Communication System: Prototype and Analysis—0
Contactless Human Activity Recognition using Deep Learning with Flexible and Scalable Software Define Radio—0
Context Aware Active Learning of Activity Recognition Models—0
Context Aware Group Activity Recognition—0
Context-Aware Query Selection for Active Learning in Event Recognition—0
Context-driven Active and Incremental Activity Recognition—0
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models—0
Contextual Relationship-based Activity Segmentation on an Event Stream in the IoT Environment with Multi-user Activities—0
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions—0
Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization—0
Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis—0
Continuous Human Activity Recognition using a MIMO Radar for Transitional Motion Analysis—0
Contrastive Learning for Time Series on Dynamic Graphs—0
Contrastive Left-Right Wearable Sensors (IMUs) Consistency Matching for HAR—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