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 551–600 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
RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonics—0
Group Activity Recognition in Basketball Tracking Data -- Neural Embeddings in Team Sports (NETS)—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
ViT-ReT: Vision and Recurrent Transformer Neural Networks for Human Activity Recognition in Videos—0
Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition—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
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications—0
Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR—0
Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain—0
Federated Continual Learning through distillation in pervasive computing—0
CHARM: A Hierarchical Deep Learning Model for Classification of Complex Human Activities Using Motion Sensors—0
SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human SensingCode2
Multi-Modal Unsupervised Pre-Training for Surgical Operating Room Workflow Analysis—0
Hunting Group Clues with Transformers for Social Group Activity Recognition—0
Fine-grained Activities of People Worldwide—0
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity RecognitionCode0
Adaptation of Surgical Activity Recognition Models Across Operating Rooms—0
WiFi-based Spatiotemporal Human Action Perception—0
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency ConsistencyCode2
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition—0
ProActive: Self-Attentive Temporal Point Process Flows for Activity SequencesCode0
Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition—0
PrivHAR: Recognizing Human Actions From Privacy-preserving Lens—0
Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs—0
Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable DataCode1
Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks—0
Benchmark of DNN Model Search at Deployment Time—0
Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V ProcessorsCode0
A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition—0
UMSNet: An Universal Multi-sensor Network for Human Activity Recognition—0
Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity RecognitionCode1
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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