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 576–600 of 1322 papers

TitleStatusHype
Unleashing the Power of Shared Label Structures for Human Activity Recognition—0
Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN—0
Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition—0
Human Activity Recognition in an Open WorldCode0
Graph Neural Network based Child Activity Recognition—0
OpenPack: A Large-scale Dataset for Recognizing Packaging Works in IoT-enabled Logistic Environments—0
Deep Learning for Inertial Sensor Alignment—0
Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches—0
Automated Level Crossing System: A Computer Vision Based Approach with Raspberry Pi Microcontroller—0
DroneAttention: Sparse Weighted Temporal Attention for Drone-Camera Based Activity Recognition—0
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight—0
Applications of human activity recognition in industrial processes -- Synergy of human and technology—0
Recognition and Prediction of Surgical Gestures and Trajectories Using Transformer Models in Robot-Assisted Surgery—0
Video-based Pose-Estimation Data as Source for Transfer Learning in Human Activity Recognition—0
Gated Recurrent Neural Networks with Weighted Time-Delay Feedback—0
Distribution estimation and change-point estimation for time series via DNN-based GANs—0
PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud InferenceCode0
Investigating Enhancements to Contrastive Predictive Coding for Human Activity RecognitionCode0
Unsupervised Deep Learning-based clustering for Human Activity RecognitionCode0
SWTF: Sparse Weighted Temporal Fusion for Drone-Based Activity Recognition—0
Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data—0
Multi-Stage Based Feature Fusion of Multi-Modal Data for Human Activity Recognition—0
XAI-BayesHAR: A novel Framework for Human Activity Recognition with Integrated Uncertainty and Shapely Values—0
Predicting User-specific Future Activities using LSTM-based Multi-label Classification—0
Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?—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