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
Human Activity Behavioural Pattern Recognition in Smarthome with Long-hour Data Collection—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
Human Activity Recognition based on Dynamic Spatio-Temporal Relations—0
Human activity recognition based on time series analysis using U-Net—0
Cross-user activity recognition via temporal relation optimal transport—0
Human Activity Recognition for Edge Devices—0
Human Activity Recognition for Mobile Robot—0
Human activity recognition from mobile inertial sensors using recurrence plots—0
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression—0
ActionNet-VE Dataset: A Dataset for Describing Visual Events by Extending VIRAT Ground 2.0—0
Human Activity Recognition Using Multichannel Convolutional Neural Network—0
CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Network—0
Human Activity Recognition with a 6.5 GHz Reconfigurable Intelligent Surface for Wi-Fi 6E—0
Human Activity Recognition Models in Ontology Networks—0
Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning—0
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks—0
Human Pose Descriptions and Subject-Focused Attention for Improved Zero-Shot Transfer in Human-Centric Classification Tasks—0
Human Activity Recognition on wrist-worn accelerometers using self-supervised neural networks—0
Human Activity Recognition Using 3D Orthogonally-projected EfficientNet on Radar Time-Range-Doppler Signature—0
Human Activity Recognition using Attribute-Based Neural Networks and Context Information—0
FMCW Radar Principles and Human Activity Recognition Systems: Foundations, Techniques, and Applications—0
Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework—0
Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data—0
Combined Static and Motion Features for Deep-Networks Based Activity Recognition in 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