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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1276–1300 of 1322 papers

TitleStatusHype
Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence—0
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders—0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications—0
Detector-Free Weakly Supervised Group Activity Recognition—0
Device-Free Human State Estimation using UWB Multi-Static Radios—0
DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition—0
DGAR: A Unified Domain Generalization Framework for RF-Enabled Human Activity Recognition—0
DIAT-μ RadHAR (micro-doppler signature dataset) & μ RadNet (a lightweight DCNN)—For human suspicious activity recognition—0
Different Approaches for Human Activity Recognition: A Survey—0
Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition—0
Differentially Private 2D Human Pose Estimation—0
Differentially Private Video Activity Recognition—0
Digging Deeper into Egocentric Gaze Prediction—0
Directional Temporal Modeling for Action Recognition—0
DISC: a Dataset for Integrated Sensing and Communication in mmWave Systems—0
Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition—0
Discriminating sensor activation in activity recognition within multi-occupancy environments based on nearby interaction—0
Discriminative Hierarchical Rank Pooling for Activity Recognition—0
Discriminative training for Convolved Multiple-Output Gaussian processes—0
Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data—0
Disparity-Augmented Trajectories for Human Activity Recognition—0
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition—0
Distributed Agent-Based Collaborative Learning in Cross-Individual Wearable Sensor-Based Human Activity Recognition—0
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing—0
Distribution estimation and change-point estimation for time series via DNN-based GANs—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