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 526–550 of 1322 papers

TitleStatusHype
Generalizable Indoor Human Activity Recognition Method Based on Micro-Doppler Corner Point Cloud and Dynamic Graph Learning—0
Generalization Ability Analysis of Through-the-Wall Radar Human Activity Recognition—0
Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition—0
Action Recognition based Industrial Safety Violation Detection—0
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors—0
Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach—0
Context Aware Group Activity Recognition—0
Context Aware Active Learning of Activity Recognition Models—0
Approaches and Applications of Early Classification of Time Series: A Review—0
Game of LLMs: Discovering Structural Constructs in Activities using Large Language Models—0
Contactless Human Activity Recognition using Deep Learning with Flexible and Scalable Software Define Radio—0
Contact-Free Multi-Target Tracking Using Distributed Massive MIMO-OFDM Communication System: Prototype and Analysis—0
Applications of human activity recognition in industrial processes -- Synergy of human and technology—0
Applications of Deep Learning for Top-View Omnidirectional Imaging: A Survey—0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring—0
Enhancing Human Action Recognition and Violence Detection Through Deep Learning Audiovisual Fusion—0
Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review—0
Concurrent Activity Recognition with Multimodal CNN-LSTM Structure—0
Application of Transfer Learning Approaches in Multimodal Wearable Human Activity Recognition—0
Complex Activity Recognition using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video—0
Application of Machine Learning Techniques in Human Activity Recognition—0
A Deep Learning Approach To Multiple Kernel Fusion—0
From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition—0
Comparative Analysis of XGBoost and Minirocket Algortihms for Human Activity Recognition—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