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 626–650 of 1322 papers

TitleStatusHype
A communication efficient distributed learning framework for smart environments—0
Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing—0
CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Network—0
CSI-Based Cross-Domain Activity Recognition via Zero-Shot Prototypical Networks—0
Human activity recognition from mobile inertial sensors using recurrence plots—0
Human Activity Recognition for Mobile Robot—0
Human Activity Recognition for Edge Devices—0
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community—0
Cross-user activity recognition via temporal relation optimal transport—0
Human activity recognition based on time series analysis using U-Net—0
Human Activity Recognition based on Dynamic Spatio-Temporal Relations—0
Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN—0
Cross-user activity recognition using deep domain adaptation with temporal relation information—0
Human Activity Prediction in Smart Home Environments with LSTM Neural Networks—0
Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks—0
A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data—0
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks—0
Human Activity Behavioural Pattern Recognition in Smarthome with Long-hour Data Collection—0
Human Activity Recognition on wrist-worn accelerometers using self-supervised neural networks—0
Human Activity Analysis and Recognition from Smartphones using Machine Learning Techniques—0
Human Activity Recognition using Attribute-Based Neural Networks and Context Information—0
Cross-position Activity Recognition with Stratified Transfer Learning—0
Human Action Attribute Learning From Video Data Using Low-Rank Representations—0
HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences—0
Cross-modal Scalable Hierarchical Clustering in Hyperbolic space—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