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 551–575 of 1322 papers

TitleStatusHype
Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance—0
Alignment-based conformance checking over probabilistic eventsCode0
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks—0
To Store or Not? Online Data Selection for Federated Learning with Limited Storage—0
Group Activity Recognition in Basketball Tracking Data -- Neural Embeddings in Team Sports (NETS)—0
RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonics—0
Attentive pooling for Group Activity Recognition—0
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning—0
SFusion: Self-attention based N-to-One Multimodal Fusion BlockCode1
Contact-Free Multi-Target Tracking Using Distributed Massive MIMO-OFDM Communication System: Prototype and Analysis—0
ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization—0
Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System—0
Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition—0
ViT-ReT: Vision and Recurrent Transformer Neural Networks for Human Activity Recognition in Videos—0
Self-Supervised Multimodal Fusion Transformer for Passive Activity Recognition—0
AI-Powered Non-Contact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing—0
Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework—0
Vision-Based Activity Recognition in Children with Autism-Related Behaviors—0
BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition—0
Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and ApplicationsCode1
Multimodal Generation of Novel Action Appearances for Synthetic-to-Real Recognition of Activities of Daily LivingCode0
Pose Uncertainty Aware Movement Synchrony Estimation via Spatial-Temporal Graph Transformer—0
Information We Can Extract About a User From 'One Minute Mobile Application Usage'—0
Domain Generalization for Activity Recognition via Adaptive Feature Fusion—0
Discovering Behavioral Predispositions in Data to Improve 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