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 1001–1025 of 1322 papers

TitleStatusHype
Model enhancement and personalization using weakly supervised learning for multi-modal mobile sensing—0
Enhancing Human Action Recognition and Violence Detection Through Deep Learning Audiovisual Fusion—0
3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks—0
3D Human motion anticipation and classification—0
A Causality-Aware Pattern Mining Scheme for Group Activity Recognition in a Pervasive Sensor Space—0
Acceleration Estimation of Signal Propagation Path Length Changes for Wireless Sensing—0
A Close Look into Human Activity Recognition Models using Deep Learning—0
A communication efficient distributed learning framework for smart environments—0
A compact sequence encoding scheme for online human activity recognition in HRI applications—0
A Comparative Study of Human Activity Recognition: Motion, Tactile, and multi-modal Approaches—0
A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures—0
A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities—0
A Comprehensive Overview on UWB Radar: Applications, Standards, Signal Processing Techniques, Datasets, Radio Chips, Trends and Future Research Directions—0
A Comprehensive Review of Automated Data Annotation Techniques in Human Activity Recognition—0
A compressive multi-kernel method for privacy-preserving machine learning—0
A Critical Analysis on Machine Learning Techniques for Video-based Human Activity Recognition of Surveillance Systems: A Review—0
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition—0
Action2Activity: Recognizing Complex Activities from Sensor Data—0
Action Classification and Highlighting in Videos—0
ActionNet-VE Dataset: A Dataset for Describing Visual Events by Extending VIRAT Ground 2.0—0
Action Recognition based Industrial Safety Violation Detection—0
Action Segmentation Using 2D Skeleton Heatmaps and Multi-Modality Fusion—0
Activity-Aware Deep Cognitive Fatigue Assessment using Wearables—0
ActivityCLIP: Enhancing Group Activity Recognition by Mining Complementary Information from Text to Supplement Image Modality—0
An Empirical Study on Activity Recognition in Long Surgical Videos—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